Protectors

• 1: Protection Method Reference
• 1.1: Protegrity Tokenization
• 1.1.1: Tokenization Support by Protegrity Products
• 1.1.2: Delimiters
• 1.1.3: Tokenization Properties
• 1.1.3.1: Data Type and Alphabet
• 1.1.3.2: Static Lookup Table (SLT) Tokenizers
• 1.1.3.3: From Left and From Right Settings
• 1.1.3.4: Internal Initialization Vector (IV)
• 1.1.3.5: Minimum and Maximum Input Length
• 1.1.3.5.1: Calculating Token Length
• 1.1.3.6: Length Preserving
• 1.1.3.7: Short Data Tokenization
• 1.1.3.8: Case-Preserving and Position-Preserving Tokenization
• 1.1.3.8.1: Case-Preserving Tokenization
• 1.1.3.8.2: Position-Preserving Tokenization
• 1.1.3.9: External Initialization Vector (EIV)
• 1.1.3.9.1: Tokenization Model with External IV
• 1.1.3.9.2: External IV Tokenization Properties
• 1.1.3.10: Truncating Whitespaces
• 1.1.4: Tokenization Types
• 1.1.4.1: Numeric (0-9)
• 1.1.4.2: Integer (0-9)
• 1.1.4.3: Credit Card
• 1.1.4.4: Alpha (A-Z)
• 1.1.4.5: Upper-Case Alpha (A-Z)
• 1.1.4.6: Alpha-Numeric (0-9, a-z, A-Z)
• 1.1.4.7: Upper-Case Alpha-Numeric (0-9, A-Z)
• 1.1.4.8: Lower ASCII
• 1.1.4.9: Datetime (YYYY-MM-DD HH:MM:SS)
• 1.1.4.10: Decimal
• 1.1.4.11: Unicode Gen2
• 1.1.4.12: Binary
• 1.1.4.13: Email
• 1.1.4.14: Printable
• 1.1.4.15: Date (YYYY-MM-DD, DD/MM/YYYY, MM.DD.YYYY)
• 1.1.4.16: Unicode
• 1.1.4.17: Unicode Base64
• 1.1.4.18:
• 1.1.4.19:
• 1.1.4.20:
• 1.1.4.21:
• 1.1.4.22:
• 1.1.4.23:
• 1.1.4.24:
• 1.1.4.25:
• 1.1.4.26:
• 1.1.4.27:
• 1.1.4.28:
• 1.1.4.29:
• 1.1.4.30:
• 1.1.4.31:
• 1.1.4.32:
• 1.1.4.33:
• 1.1.4.34:
• 1.1.4.35:
• 1.1.4.36:
• 1.1.4.37:
• 1.1.4.38:
• 1.1.4.39:
• 1.1.4.40:
• 1.1.4.41:
• 1.1.4.42:
• 1.1.4.43:
• 1.1.4.44:
• 1.1.4.45:
• 1.1.4.46:
• 1.1.4.47:
• 1.1.4.48:
• 1.1.4.49:
• 1.1.4.50:
• 1.1.4.51:
• 1.1.5:
• 1.1.6:
• 1.2: Protegrity Format Preserving Encryption
• 1.2.1: FPE Properties
• 1.2.2: Code Points
• 1.2.3: Tweak Input
• 1.2.4: Left and Right Settings
• 1.2.5: Handling Special Numeric Credit Card Data
• 1.3: Protegrity Encryption
• 1.3.1: Encryption Algorithms
• 1.3.1.1: AES-128 and AES-256
• 1.3.1.2: CUSP
• 1.3.1.3: 3DES
• 1.3.2: Encryption Properties - IV, CRC, Key ID
• 1.3.3: Data Length and Padding in Encryption
• 1.3.4:
• 1.3.5:
• 1.3.6:
• 1.4: No Encryption
• 1.5: Monitoring
• 1.6: Masking
• 1.7: Hashing
• 1.8: ASCII Character Codes
• 1.9: Examples of Column Sized Calculation for AES and 3DES Encryption
• 1.10: Empty String Handling by Protectors
• 1.11: Hashing Functions and Examples
• 1.11.1: Hash Data column size
• 1.11.2: Using Hashing Triggers and View
• 1.12: Codebook Re-shuffling in the Data Security Gateway
• 1.13:
• 1.14:
• 1.15:
• 1.16:
• 2: Application Protector
• 2.1: Application Protector Java
• 2.1.1: Understanding the Architecture
• 2.1.2: System Requirements
• 2.1.3: Preparing the Environment
• 2.1.4: Installing the AP Java Protector
• 2.1.5: Configuring the Protector
• 2.1.6: Upgrading the Application Protector Java
• 2.1.6.1: About Upgrade Agent
• 2.1.6.2: Installing the Upgrade Agent
• 2.1.6.3: Setting Up the Upgrade Agent
• 2.1.6.3.1: Upgrade Configurations
• 2.1.6.3.2: Rollback Behavior
• 2.1.6.3.3: Protegrity SDK Upgrade Permissions and Deployment
• 2.1.6.3.3.1: User Roles and Groups
• 2.1.6.3.3.2: Component Overview
• 2.1.6.3.3.3: Recommended File and Folder Permissions
• 2.1.6.4: AP Java Upgrade and Rollback Examples
• 2.1.6.4.1: Online and Offline Upgrade
• 2.1.6.4.2: Online and Offline Rollback
• 2.1.7: Application Protector Java APIs
• 2.1.7.1: Using the AP Java APIs
• 2.1.8: Additional Topics
• 2.1.8.1: Memory Usage of the AP Java
• 2.1.8.2: DevOps Approach for Application Protector Java
• 2.1.8.3: Application Protector API Return Codes
• 2.1.8.4: Config.ini file for Application Protector
• 2.1.8.5: Multi-node Application Protector Architecture
• 2.1.8.6: Installation Directory Structure Overview
• 2.1.8.7: SDK Upgrader Agent Configuration File
• 2.1.8.8: Troubleshooting for AP Java Upgrade
• 2.1.8.9: Uninstalling the Application Protector
• 3: Big Data Protector
• 3.1: CDP-PVC-Base
• 3.1.1: Understanding the architecture
• 3.1.2: System Requirements
• 3.1.3: Preparing the Environment
• 3.1.3.1: Extracting the installation package
• 3.1.3.2: Running the configurator script
• 3.1.3.3: Setting up the parcels
• 3.1.3.4: Distributing the parcels
• 3.1.3.5: Activating the parcels
• 3.1.4: Installing the Big Data Protector
• 3.1.5: Configuring the Big Data Protector
• 3.1.5.1: Registering the UDFs using Helper scripts
• 3.1.5.1.1: Registering and dropping the Hive UDFs
• 3.1.5.1.2: Registering the Spark UDFs
• 3.1.5.1.3: Registering and dropping the Impala UDFs
• 3.1.5.1.4: Installing the Impala UDFs
• 3.1.5.2: Updating the parcels
• 3.1.5.2.1: Updating the Certificates Parcel With a restart
• 3.1.5.2.2: Updating the Certificates Parcel Without a restart
• 3.1.5.2.3: Updating the log forwarder parcel
• 3.1.5.3: Updating the configuration parameters
• 3.1.5.3.1: Setting the Big Data Protector configuration
• 3.1.5.3.2: Upading Parameters in the config.ini file
• 3.1.5.3.3: Upading Parameters for the RPAgent
• 3.1.5.3.4: Upading Parameters for the Log Forwarder
• 3.1.5.3.5: Adding a new configuration parameter
• 3.1.6: Upgrading the Big Data Protector
• 3.1.6.1: Editing the Cluster Configuration File
• 3.1.6.2: Executing the Upgrade Script
• 3.1.6.3: Downgrading to an older version
• 3.1.7: Uninstalling the Big Data Protector
• 3.1.7.1: Uninstalling the Impala UDFs
• 3.1.7.2: Restoring the Big Data Protector configuration
• 3.1.7.3: Removing the Big Data Protector Services
• 3.1.7.4: Deactivating the parcels
• 3.1.7.5: Removing the parcels
• 3.1.7.6: Deleting the parcels from the local repository
• 3.1.7.7: Deleting the CSD files
• 3.2: Amazon Elastic MapReduce Protector
• 3.2.1: Understanding the architecture
• 3.2.1.1: Bootstrap installer architecture
• 3.2.1.2: Static installer architecture
• 3.2.1.3: EMR Serverless architecture
• 3.2.2: Preparing the environment
• 3.2.2.1: Setting up for the Bootstrap Installer
• 3.2.2.1.1: Verifying the prerequisites
• 3.2.2.1.2: Extracting the Big Data Protector Package
• 3.2.2.1.3: Executing the Configurator Script
• 3.2.2.2: Setting up for the Static Installer
• 3.2.2.2.1: Verifying the prerequisites for Static Installer
• 3.2.2.2.2: Extracting the Installation Package
• 3.2.2.2.3: Updating the BDP.Config File
• 3.2.2.3: Setting up for the EMR Serverless Installer
• 3.2.2.3.1: Extracting the Big Data Protector Package
• 3.2.2.3.2: Executing the Configurator Script
• 3.2.3: Installing the protector
• 3.2.3.1: Using the Bootstrap Installer
• 3.2.3.1.1: Creating a Cluster
• 3.2.3.1.2: Managing the Cluster Nodes
• 3.2.3.1.3: Verifying the Parameters
• 3.2.3.2: Using the Static Installer
• 3.2.3.2.1: Installing the Protector on all the Nodes
• 3.2.3.2.2: Installing the Protector on Specific Nodes
• 3.2.3.2.3: Verifying the Parameters
• 3.2.3.3: Using the EMR Serverless Installer
• 3.2.3.3.1: EMR Serverless Setup CLI
• 3.2.3.3.2: Setting up the Log Forwarder
• 3.2.3.3.3: Performing URP Operations
• 3.2.4: Configuring the protector
• 3.2.5: Working with Cluster Utilities
• 3.2.5.1: RPAgent Control Script
• 3.2.5.2: Log Forwarder Control Script
• 3.2.5.3: Sync Config.ini
• 3.2.5.4: Sync Log Forwarder Configuration
• 3.2.5.5: Sync RPAgent Configuration
• 3.2.6: Uninstalling the protector
• 3.2.6.1: Uninstalling the Big Data Protector when Bootstrap is used
• 3.2.6.2: Uninstalling the Big Data Protector when Static installer is used
• 3.2.6.2.1: From all the Nodes
• 3.2.6.2.2: From Specific Nodes
• 3.2.6.3: Uninstalling the Big Data Protector when Serverless is used
• 3.3: User Defined Functions and APIs
• 3.3.1: MapReduce APIs
• 3.3.2: Hive UDFs
• 3.3.3: Pig UDFs
• 3.3.4: HBase Commands
• 3.3.5: Impala UDFs
• 3.3.6: Spark Java APIs
• 3.3.7: Spark SQL UDFs
• 3.3.8: PySpark - Scala Wrapper UDFs
• 3.3.9: Unity Catalog Batch Python UDFs
• 3.4: Additional Information
• 3.4.1: Migrating Tokenized Unicode Data
• 3.4.2: Return Codes for the Big Data Protector
• 4: Database Protector
• 4.1: Oracle Database Protector
• 4.1.1: Understanding the Architecture
• 4.1.2: System Requirements
• 4.1.3: Preparing the Environment
• 4.1.3.1: Extracting the Installation Package
• 4.1.3.2: Installing the Log Forwarder
• 4.1.3.3: Installing the RPAgent
• 4.1.4: Installing Oracle Database Protector
• 4.1.4.1: Installing the Database Objects
• 4.1.4.2: Installing Oracle Database Protector on Standalone System
• 4.1.4.3: Installing the Oracle Database Protector on RAC (Multinode) system
• 4.1.4.4: Creating the User Defined Functions (UDFs)
• 4.1.5: Configuring the Oracle Database Protector
• 4.1.5.1: User Impersonation
• 4.1.5.2: Enterprise User Security (EUS) in the Oracle Database
• 4.1.5.3: Troubleshooting
• 4.1.6: Upgrading the Oracle Database Protector
• 4.1.6.1: Upgrading the Oracle Database Protector on Standalone system
• 4.1.6.2: Creating the UDFs after Upgrade
• 4.1.6.3: Upgrading the Oracle Database Protector on RAC system
• 4.1.7: Uninstalling the Oracle Database Protector
• 4.1.7.1: Dropping User Defined Functions
• 4.1.7.2: Uninstalling the RPAgent
• 4.1.7.3: Uninstalling the Log Forwarder
• 4.2: User Defined Functions and APIs
• 4.2.1: Oracle User Defined Functions and APIs
• 4.2.1.1: General UDFs
• 4.2.1.2: Access Check Procedures
• 4.2.1.3: Insert Encryption UDFs
• 4.2.1.4: Insert No-Encryption, Token, and FPE UDFs
• 4.2.1.5: Multiple Insert Encryption Procedures
• 4.2.1.6: Select Decryption UDFs
• 4.2.1.7: Select No-Encryption, Token, and FPE UDFs
• 4.2.1.8: Update Encryption UDFs
• 4.2.1.9: Update No-Encryption, Token, and FPE UDFs
• 4.2.1.10: Multiple Update Encryption Procedures
• 4.2.1.11: Hash UDFs
• 4.2.1.12: Blob UDFs
• 4.2.1.13: Clob UDFs
• 4.2.1.14: Bulk UDFs
• 4.2.1.15: Oracle Input Datatype to UDF Mapping
• 5: Data Warehouse Protectors
• 5.1: Teradata Data Warehouse Protector
• 5.1.1: Understanding the Teradata Architecture
• 5.1.2: System Requirements
• 5.1.3: Preparing the Environment
• 5.1.3.1: Extracting the Installation Package
• 5.1.3.2: Installing the Log Forwarder
• 5.1.3.3: Installing the Resilient Package Agent
• 5.1.4: Installing the Teradata Data Warehouse Protector
• 5.1.4.1: Installing the Objects
• 5.1.4.2: Installing the Protector on Single Node
• 5.1.4.3: Installing the Protector on Multi Node
• 5.1.4.4: Creating the User Defined Functions
• 5.1.4.5: User Defined Types
• 5.1.5: Configuring the Teradata Data Warehouse Protector
• 5.1.5.1: Updating the Config.ini File
• 5.1.5.2: Updating the Output Buffer
• 5.1.5.3: Troubleshooting
• 5.1.6: Upgrading the Teradata Data Warehouse Protector
• 5.1.6.1: Upgrading the Protector on Single Node
• 5.1.6.2: Upgrading the Protector on Multi Node
• 5.1.7: Uninstalling the Teradata Data Warehouse Protector
• 5.1.7.1: Uninstalling the Log Forwarder
• 5.1.7.2: Uninstalling the RPAgent
• 5.1.7.3: Dropping the User Defined Functions
• 5.1.7.4: Removing the Installation Directory
• 5.1.8: Appendix
• 5.1.8.1: Additional references for the Protectors
• 5.1.8.1.1: Additional references for the Teradata Protector
• 5.1.8.1.1.1: Configuring access to execute queries
• 5.1.8.1.1.2: Teradata Query Bands and Trusted Sessions
• 5.1.8.2: Data Warehouse Sample Scripts
• 5.1.8.2.1: Teradata Database Data Warehouse Sample Scripts
• 5.2: User Defined Functions and APIs
• 5.2.1: Teradata UDFs
• 5.2.1.1: General UDFs
• 5.2.1.2: Access Check UDFs
• 5.2.1.3: Varchar Latin UDFs
• 5.2.1.4: Varchar Unicode UDFs
• 5.2.1.5: Float UDFs
• 5.2.1.6: Small Integer UDFs
• 5.2.1.7: Integer UDFs
• 5.2.1.8: Big Integer UDFs
• 5.2.1.9: Date UDFs
• 5.2.1.10: 8-Byte and 16-Byte Decimal UDFs
• 5.2.1.11: JSON UDFs
• 5.2.1.12: XML UDFs
• 5.2.1.13: Float UDFs for No Encryption
• 5.2.1.14: Date UDFs for No Encryption
• 5.2.1.15: 8-Byte AND 16-Byte Decimal UDFs for No Encryption
• 6: REST Container
• 6.1: Understanding the Architecture
• 6.1.1: Architecture and Components using Dynamic-based Deployment
• 6.1.2: Architecture and Components using Static Deployment
• 6.2: System Requirements
• 6.2.1: Software Requirements
• 6.2.2: Hardware Requirements
• 6.3: Preparing the Environment
• 6.3.1: Initializing the Jump Box
• 6.3.2: Extracting the Installation Package
• 6.3.3: Creating Certificates
• 6.3.4: Uploading the Images to the Container Repository
• 6.3.5: Creating the AWS Environment
• 6.3.5.1: Creating the AWS Setup for Static Mode
• 6.3.5.1.1: Creating an Data Encryption Key (DEK)
• 6.3.5.1.2: Creating an AWS S3 Bucket
• 6.3.5.1.3: Creating an AWS EFS
• 6.3.5.2: Creating a Kubernetes Cluster
• 6.4: Installing the Protector
• 6.4.1: Deploying REST Container for Dynamic Method
• 6.4.1.1: Deploying Log Forwarder
• 6.4.1.2: Deploying Resilient Package Proxy (RPP)
• 6.4.1.3: Deploying the REST Container with Dynamic Method
• 6.4.1.4: Uninstalling the Protector in Dynamic Method
• 6.4.2: Deploying REST Product in Static Mode
• 6.4.2.1: Retrieving the Policy Package from the ESA
• 6.4.2.2: Deploying Log Forwarder
• 6.4.2.3: Deploying KMSProxy Container
• 6.4.2.4: Deploying REST Container Using Static Method
• 6.4.2.5: Updating the Policy Package
• 6.4.2.6: Uninstalling the Protector in Static Method
• 6.5: Application Protector API on REST
• 6.5.1: Version 4 (V4) Application Protector API on REST
• 6.5.1.1: List of REST APIs
• 6.5.1.1.1: HTTP GET version
• 6.5.1.1.2: HTTP POST protect
• 6.5.1.1.3: HTTP POST unprotect
• 6.5.1.1.4: HTTP POST reprotect
• 6.5.1.1.5: HTTP GET doc
• 6.5.1.1.6: HTTP Headers
• 6.5.1.2: V4 AP REST HTTP Response Codes
• 6.5.2: Version 1 (V1) Application Protector API on REST
• 6.5.2.1: List of REST APIs
• 6.5.2.1.1: HTTP GET version
• 6.5.2.1.2: HTTP POST protect
• 6.5.2.1.3: HTTP POST unprotect
• 6.5.2.1.4: HTTP POST reprotect
• 6.5.2.1.5: HTTP Headers
• 6.5.2.2: Error Handling for v1 API
• 6.5.2.3: V1 AP REST HTTP Response Codes
• 6.6: Using Samples
• 6.7: Running the Autoscaling Script
• 6.8: Upgrading the Protector from Version 9.x to 10.x
• 6.9: Upgrading the Protector from Version 10.x to 10.y
• 6.10: Using Dockerfiles to Build Custom Images
• 6.11: Appendix - Deploying the Helm Charts by Using the Set Argument
• 7: Application Protector Java Container
• 7.1: Understanding the Architecture
• 7.1.1: Architecture and Components using Dynamic-based Deployment
• 7.1.2: Architecture and Components using Static Deployment
• 7.2: System Requirements
• 7.2.1: Software Requirements
• 7.2.2: Hardware Requirements
• 7.3: Preparing the Environment
• 7.3.1: Initializing the Jump Box
• 7.3.2: Extracting the Installation Package
• 7.3.3: Creating Certificates
• 7.3.4: Uploading the Images to the Container Repository
• 7.3.5: Creating the AWS Environment
• 7.3.5.1: Creating the AWS Setup for Static Mode
• 7.3.5.1.1: Creating a Data Encryption Key (DEK)
• 7.3.5.1.2: Creating an AWS S3 Bucket
• 7.3.5.1.3: Creating an AWS EFS
• 7.3.5.2: Creating a Kubernetes Cluster
• 7.4: Installing the Protector
• 7.4.1: Deploying AP Java Container for Dynamic Method
• 7.4.1.1: Deploying Log Forwarder
• 7.4.1.2: Deploying Resilient Package Proxy (RPP)
• 7.4.1.3: Deploying the AP Java Container with Dynamic Method
• 7.4.1.4: Uninstalling the Protector in Dynamic Method
• 7.4.2: Deploying AP Java Container in Static Mode
• 7.4.2.1: Retrieving the Policy Package from the ESA
• 7.4.2.2: Deploying Log Forwarder
• 7.4.2.3: Deploying KMSProxy Container
• 7.4.2.4: Deploying AP Java Container Using Static Method
• 7.4.2.5: Updating the Policy Package
• 7.4.2.6: Uninstalling the Protector in Static Method
• 7.5: Running Security Operations
• 7.6: Upgrading the Protector from Version 9.x to 10.x
• 7.7: Upgrading the Protector from Version 10.x to 10.y
• 7.8: Using Dockerfiles to Build Custom Images
• 7.9: Appendix - Deploying the Helm Charts by Using the Set Argument

1 - Protection Method Reference

Protegrity products can protect sensitive data with the following protection methods:

• Tokenization
• Format Preserving Encryption (FPE)
• Encryption
• Hashing
• No Encryption
• Monitoring
• Masking

The following table describes the protection methods for structured and unstructured data security policy types.

Table: Protection Methods by Data Security Policy Type

The following table describes the deprecated protection methods for structured and unstructured data security policy types.

Table: Deprecated Protection Methods by Data Security Policy Type

Protegrity protection methods, including tokenization, encryption, monitoring, masking, and hashing, support various input formats. This enables you to protect sensitive data using these methods. Some examples of input formats are as follows:

• Social Security Numbers (SSNs)
• Credit Card Numbers (CCNs)
• Electronic Personal Health Information (ePHI), which is controlled by Health Insurance Portability and Accountability Act (HIPPA) and Health Information Technology for Economic and Clinical Health (HITECH)
• Personally identifiable information (PII)

The following table shows different types of sensitive data that can be protected using different protection methods. It demonstrates input values and their corresponding protected values.

Table: Examples of Protected Data

You can combine Protegrity protection methods to obtain the required level of data access control within the enterprise.

For example, a Security Officer can use a data security policy to control what is delivered to different roles in the policy. The following figure shows how Social Security Number access can vary by different users and applications.

In the figure, the tokenized SSN is stored in the database. However, there are four roles defined in the policy:

Table: Different Roles in the Policy

Each role can receive a different form of the SSN based on its need. The Security Officer determines the SSN form by role.
Protegrity tokenization maintains a separation of duties by way of the data security policy.
The DBA, Developers, and System Administrators do not have direct access to the data. Everything goes through the data security policy, regardless of who manages the system.
For more information about data security policies, refer to Managing policies.

1.1 - Protegrity Tokenization

Tokenization is the process of replacing sensitive data with tokens that has no worth to someone who gains unauthorized access to the data. With tokenization, specific pieces of original data can be preserved, while the system tokenizes data according to design. Tokens can be set up and deployed directly on the protectors, depending on your enterprise configuration and data security needs. Once tokenization is deployed, operational systems continually work with the tokens. If the operational systems experience a security breach, then only the tokens are at risk of being compromised. Protegrity tokenization is transparent to end-users. Data integrity is strongly enforced by way of the data security policy.

Protegrity tokenization can be configured to preserve different parts of the original value in the token, such as the last 4 digits. It also recognizes and preserves delimiters, which are often used in SSNs, dates, etc.

Protegrity tokenization enables the user to tokenize various input data types, such as payment card industry (PCI), personally identifiable information (PII), and protected health information (PHI).

With Protegrity tokenization, there is a 1:1 relationship between the real data value and its token value. This enables token values to be used as alternative unique IDs that can be used for joining related information.

The following table describes the token types supported by Protegrity tokenization.

Table: Tokenization Types

The following table describes the deprecated token types supported by Protegrity tokenization.

1.1.1 - Tokenization Support by Protegrity Products

Protegrity offers various types of protectors which helps to protect data in different software and platforms. For example, we can use:

• Application Protectors: To protect data in C, C++, Python, Java, .Net, and Go programming languages.
• Big Data Protectors: To protect data in Big Data at various component levels, such as, Hive, Pig, MapReduce, etc.
• Data Warehouse Protectors: To protect data in the Teradata Data Warehouses.
• Gateway Protectors: To protect data in Gateway Protectors like Data Security Gateway (DSG).
• Cloud Protectors: To protect data in Cloud Protectors.

Each protector has certain tokenization types which are listed in the following sections.

Application Protector

The Protegrity Application Protector (AP) is a high-performance, versatile solution that provides a packaged interface to integrate comprehensive, granular security and auditing into enterprise applications.

Application Protectors support all types of tokens.

Table: Supported Tokenization Types by Application Protector

^*1 - If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

Table: Deprecated Tokenization Types supported by Application Protector

^*1 - If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows the tokenization types supported for Big Data Protectors.

Table: Supported Tokenization Types for Big Data Protectors

^*1 - The customer application should convert the input into a byte array and generate the output from the byte array in the required data type.
^*2 - The Datetime tokenization will only work with VARCHAR data type.
^*3 - The Char tokenization UDFs only support Numeric, Alpha, Alpha Numeric, Upper-case Alpha, Upper Alpha-Numeric, and Email data elements, and with length preservation selected. Using any other data elements with Char tokenization UDFs is not supported. Using non-length preserving data elements with Char tokenization UDFs is not supported.

The following table shows the deprecated tokenization types supported for Big Data Protectors.

Table: Deprecated Tokenization Types supported for Big Data Protectors

^*1 - The customer application should convert the input into a byte array and generate the output from the byte array in the required data type.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

Table: Supported Tokenization Types for Data Warehouse Protector

Table: Deprecated Tokenization Types supported by Data Warehouse Protector

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

• If you have fixed-length data fields and the input data is shorter than the length of the field, then truncate the leading and trailing white spaces before passing the input to the respective Protect and Unprotect UDFs.
• The truncation of whitespaces ensures consistent data output for the protect and unprotect operations. This consistency holds true across all Protegrity products.
• For more information, refer to Truncating Whitespaces.

Database Protector

Oracle Database Protector

1.1.2 - Delimiters

Protegrity tokenization can generate the same token regardless of how the data is formatted. Any character in the input that does not comply with the token types in the Tokenization Types is generally treated as a delimiter and remains unchanged during tokenization.

The following table shows how the Protegrity Token types handles delimiters and spaces as compared to plain numerical data.

Table: Tokenization with Delimiters

Note: Some tokenizers can tokenize delimiters. Unicode Gen2, lower ASCII, printable, and binary are examples of tokenizers that can tokenize delimiters.

1.1.3 - Tokenization Properties

Table: Common Tokenization Properties

The following table shows what properties can be set for the token types.

Table: Tokenization Properties for Token Types

• X - means that Property is disabled and cannot be specified.
• √ - means that Property is enabled or can be specified.

The following table shows what properties can be set for the deprecated token types.

Table: Tokenization Properties for deprecated Token Types

• X - means that Property is disabled and cannot be specified.
• √ - means that Property is enabled or can be specified.

1.1.3.1 - Data Type and Alphabet

An alphabet contains all characters considered for tokenization, it is derived from the tokenization type. Characters outside the alphabet are considered delimiters.

Note: This is applicable only for Unicode Gen2 token.

Refer to Tokenization Types for the full list of supported token types.

1.1.3.2 - Static Lookup Table (SLT) Tokenizers

A static lookup table (SLT) contains a pre-generated list of all possible values from a given set of characters. An alphabetic lookup table for instance might contain all values from “Aa” to “Zz”. All entries are then shuffled so that they are in random order.

SLT tokenizer uses multiple SLTs to generate tokens. This is done by first dividing the input value into smaller pieces, called token blocks, which correspond to entries in the lookup tables. The token blocks are then substituted with values from the SLTs and chained together to form the final token value. This means that the token is a result of multiple lookups in multiple SLTs.

Another benefit of SLT tokenizers is that tokenization can be done locally on the protector. With this solution, tokenization is performed locally within the protector environment.

For more information, refer to Working with Data Elements.

There are several types of SLT tokenizers from which you can choose. They are distinguished by their block size and the number of lookup tables.

Table: SLT Tokenizer with block size and lookup tables

*1 - For the SLT_X_1 tokenizer, the number of lookup tables used for the security operations is determined during the creation of the data elements.

The following table describes the types of SLT tokenizers and compares their characteristics.

Table: SLT Tokenizer Memory Footprint for Token Types

Note: The amount of memory used in the protector is twice the size of the token tables (kB) because an inverted SLT is stored in the memory, in addition to the original SLT.

Table: SLT Tokenizer Characteristics for Deprecated Token Types

1.1.3.3 - From Left and From Right Settings

This property indicates the number of characters from left and right that will remain in the clear and hence be excluded from tokenization. Not all token types will allow the end-user to specify these values. The From Left and From Right settings can be configured in the Tokenize Options during the Data Element creation on the ESA Web UI.

For example;
Input Value: 5511309239934975
Credit Card Token: Left=0 Right=4
Output Value: 8278278929904975

When processing input data, you must check the From Left and From Right settings. Validate the input data based on the From Left and From Right settings before applying the Allow Short Data settings.

For more information about how From Left and From Right settings work together with short data settings, refer to Calculating Token Length.

1.1.3.4 - Internal Initialization Vector (IV)

Internal IV is automatically applied to the input value when the token element’s left and right properties are non-zero, designating some characters to remain in the clear. An Internal IV provides an additional security during the tokenization process.

Data to tokenize can be logically divided into three components: left, middle, and right. If an IV is used, then the left and right components are concatenated to form the IV. This IV is then added to the middle component before the value is tokenized.

Table: Examples of Tokenization with Internal IV

1.1.3.5 - Minimum and Maximum Input Length

In Protegrity tokenization only the Decimal token type allows for defining the Minimum and Maximum length of the token element when created. Some token types, such as Datetime, have a fixed length. For the remainder, Minimum and Maximum length depends on token type, tokenizer, length preservation, and short token setting.

The following table illustrates length settings by token type.

Table: Minimum and Maximum Input Length for Token Types

• The minimum and maximum length validation on input data is done on the characters to tokenize.
• The From Left and From right clear characters are not counted. Additionally, characters outside of the alphabet for the selected token type are also not counted.
• The NULL values are accepted but not tokenized.

Table: Minimum and Maximum Input Length for Deprecated Token Types

1.1.3.5.1 - Calculating Token Length

For a Numeric token type, non-numeric values are considered as delimiters. The unsupported characters will be treated as delimiters and left un-tokenized. This occurs when the input value does not contain tokenizable characters with the selected token type.

The number of characters to tokenize is calculated as described on the following image:

If the input value does not contain characters to tokenize, then it is considered a zero-length token. The tokenization of a zero-length input value will not produce an error during the tokenization, and input value will be returned as output.

If the input value has at least one character and short data tokenization is enabled, then the source data can be tokenized. If short data tokenization is not enabled, then the source data will be returned as it is. Alternatively, an appropriate error will appear due to tokenization.

For more information on short data tokenization, refer to Short Data Tokenization.

If the input value contains more characters than the maximum for tokenization, then the value of tokenization is considered too long. The tokenization process provides an appropriate error message.

If the input value has a sufficient number of characters, the tokenization process is successful. This occurs when the character count falls between the minimum and maximum settings.

Table: Token Length Examples

1.1.3.6 - Length Preserving

With the Preserve Length flag enabled, the length of the input data and protected token value is the same.

For data elements with the Preserve Length flag available, you have an option to generate token values that are of the same length as the input data.

Note: The Unicode Gen2 token element is Code Point length preserving when this option is enabled. The length in bytes can vary depending on the alphabet selected during data element creation.

As an extension to this flag, the Allow Short Data flag provides multiple options to manage short input data handling. If the Preserve Length property is not set, then short input protected will not keep its original length. Generated tokens will at least have the minimum length defined for the token type.

For more information about short data tokenization, refer to Short Data Tokenization.

A check for maximum input length is performed regardless of the preservation setting. This check ensures that the input is within the allowed length limit.

If Preserve Length is not selected, then tokenized data may be longer than the input value up to +5%, or at least +1 symbol on a very small initial value (1-2 symbols). Here, symbol can represent a character or a code point.

If Preserve Length is not selected, then for applying protection in database columns, column length of the resulting protected table should be bigger than length of the column to tokenize in the initial table. This will allow inserting tokenized data during protection when tokenized data is longer than the input data.

1.1.3.7 - Short Data Tokenization

When using tokenizers, such as, SLT_1_3, SLT_2_3, and SLT_X_1, the minimum input limit for tokenizable characters or bytes is three. When using tokenizers, such as, SLT_1_6 and SLT_2_6, the minimum input limit for tokenizable characters or bytes is six.

The possible flag values for short data tokenization are described in the following table.

Table: Short tokens flag values

The following tokens support short data tokenization:

• Numeric (0-9)
• Alpha (a-z, A-Z)
• Upper-case Alpha (A-Z)
• Alpha-Numeric (0-9, a-z, A-Z)
• Upper-Case Alpha-Numeric (0-9, A-Z)
• Lower ASCII
• Email
• Unicode Gen2

The following deprecated tokens support short data tokenization:

• Printable
• Unicode
• Unicode Base64

Important: Short input data tokenization can be at risk as a user can easily guess the lookup table and the original data by tokenizing some input data. Consider carefully before using the short data tokenization. If possible, short data input must be avoided.

For more information about the maximum length setting for non-length-preserving token elements, refer to Minimum and Maximum Input Length by Token Types.

1.1.3.8 - Case-Preserving and Position-Preserving Tokenization

This section explains the Case-Preserving and Position-Preserving tokenization options.

• Case-Preserving and Position-Preserving tokenization was designed to support specific business requirements. However, this design comes with a trade-off, as it affects the cryptographic strength of the tokens.
• When preserving the case and position of Alpha-Numeric characters, some information may be leaked through the tokenized value.
• In addition, depending on the length of the Alpha and Numeric substrings, tokens may suffer the same weaknesses as Short Tokens, as described in the section Short Data Tokenization.
• It is recommended that this method should not be used for most use cases. Before using this method, contact Protegrity Support to ensure that the risks are fully understood.

1.1.3.8.1 - Case-Preserving Tokenization

When working with data that is received from multiple sources, the data can contain different casing properties. The data processing stage makes the casing consistent prior to distributing the data to additional systems.

If tokenization is performed prior to the data processing stage, then it results in tokens that differ in its casing properties as per the non-processed data.

To preserve the casing of the non-processed data while tokenizing, an additional tokenization option is provided for the Alpha-Numeric (0-9, a-z, A-Z) token type. The casing of the alphabets in the tokenized value matches the casing of the alphabets in the input value.

Note:
You can specify the case-preserving tokenization option when using the SLT_2_3 tokenizer and Alpha-Numeric (0-9, a-z, A-Z) token type only.
If you select the Preserve Case property on the ESA Web UI, then the Preserve Position property is also selected, by default. Hence, the position of the alphabets and numbers is preserved along with the casing of the alphabets in the output tokenized value.
If you are selecting the Preserve Case or Preserve Position property on the ESA Web UI, then the following additional properties are set:

• The Preserve Length property is enabled and Allow Short Data property is set to Yes, by default. These two properties are not modifiable.
• The retention of characters or digits from the left and the right are disabled, by default. The From Left and From Right properties are both set to zero.

For more information about specifying the case-preserving tokenization option for the Alpha-Numeric (0-9, a-z, A-Z) token type, refer to Create Token Data Elements.

The following table provides some examples for the case-preserving tokenization option.

Table: Case-Preserving Tokenization Examples

1.1.3.8.2 - Position-Preserving Tokenization

The alphabetic and numeric positions in the position-preserving tokenized value matches the alphabetic and numeric positions in the input value.

You can specify the position-preserving tokenization option when using the SLT_2_3 tokenizer and Alpha-Numeric (0-9, a-z, A-Z) token type only.
If you are selecting the Preserve Case or Preserve Position property, then the following additional properties are set:

• The Preserve Length property is enabled and Allow Short Data property is set to Yes, by default. These two properties are not modifiable.
• The retention of characters or digits from the left and the right are disabled, by default. The From Left and From Right properties are both set to zero.

For more information about specifying the position-preserving tokenization option for the Alpha-Numeric (0-9, a-z, A-Z) token type, refer to Create Token Data Elements.

The following table provides some examples for the position-preserving tokenization option.

Table: Position-Preserving Tokenization Examples

1.1.3.9 - External Initialization Vector (EIV)

1.1.3.9.1 - Tokenization Model with External IV

The External IV value is set as a new parameter when calling protect, unprotect or reprotect API from the client application.

The following example explains how the tokenization is performed with the External IV defined. As mentioned before, the main characteristic of the External IV feature is obtaining different outputs for the same input. To have different outputs, you need to specify different IVs.

Note: The External IV is used, prior to protection, as input to modify the data to protect. The External IV is ignored when using encryption.

1.1.3.9.2 - External IV Tokenization Properties

The tokenization with the External IV is done only if the IV is specified during the protect operation through the end user API. When performing unprotect and re-protect operations, the same IV value used for protection must be identified.

If External IV is not provided in either a protect or unprotect function call, then the input is tokenized as-is without any IV.

The External IV value has the following properties:

• Supports ASCII and Unicode characters.
• Minimum 1 byte for the input.
• Maximum 256 bytes for the input.
• Empty and NULL strings are not supported as External IV values. These strings will be ignored during tokenization. The process will continue as if External IV was not used.

Here is an example of the tokenized input value with the External IV for a Numeric token:

Table: Example-External IV for a Numeric token

1.1.3.10 - Truncating Whitespaces

With fixed length fields or columns, input data may be shorter than the length of the field. When this happens, data may be appended with either, or both, trailing and leading whitespace. In those situations, the whitespace is considered during Tokenization. It will affect the tokenization results.

For instance, consider a scenario where the name “Hultgren Caylor” is stored in a Hive Char(30) column.

As the length of the data is less than 30 characters, trailing whitespaces are appended to it. In this case, assume that we need to protect this column with a data element that preserves the first and last character (L=1, R=1). Now with this setting, the expectation is to preserve character H at the start and the character r at the end, in the protected value output. However, the actual data has trailing whitespaces. This results in the output containing the character “H” at the start and a whitespace character " " at the end. The unnecessary trailing whitespaces cause the final protected output to generate a different token.

It is recommended to truncate trailing and leading whitespaces from the data. This applies before sending the data to Protect, Unprotect, or Reprotect UDFs. Truncating unnecessary whitespaces ensures that only the actual data is considered during tokenization. Any trailing and leading whitespaces are not taken into account.

In addition, it is important to follow a consistent approach for truncating the whitespaces across all operations, such as, Protect, Unprotect, Reprotect. For instance, if we have truncated unnecessary trailing whitespaces from the input before the Protect operation, then the same logic of truncating whitespaces from the input, during Unprotect and Reprotect operations needs to be followed.

1.1.4 - Tokenization Types

1.1.4.1 - Numeric (0-9)

The Numeric token type tokenizes digits from 0 to 9.

Table: Numeric Tokenization Type properties

The following table lists the examples of numeric tokenization values.

Table: Examples of Numeric tokenization values

Numeric Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Numeric token.

Table: Supported input data types for Application protectors with Numeric token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protector only supports bytes converted from the string data type. If any other data type is directly converted to bytes and passed as input to the Application Protectors APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Numeric token.

Table: Supported input data types for Big Data protectors with Numeric token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

^*3 – If you are using the Char tokenization UDFs in Hive, then ensure that the data elements have length preservation selected. In Char tokenization UDFs, using data elements without length preservation selected, is not supported.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Numeric token.

Table: Supported input data types for Data Warehouse protectors with Numeric token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

Note: For numeric data elements where length preservation is not enabled, the maximum supported length is 3,842 characters. Data up to this length can be tokenized and de-tokenized without errors.

1.1.4.2 - Integer (0-9)

The Integer token type tokenizes 2, 4, or 8 byte size integers.

Table: Integer Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Integer token.

Table: Examples of Integer tokenization values

The pty.ins_integer UDF in the Oracle, Teradata, and Impala Protectors, supports input data length of 4 bytes only. For 2 bytes, the following error is returned: Invalid input size.

Integer Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Integer token.

Table: Supported input data types for Application protectors with Integer token

If the user passes a 4-byte integer with values ranging from -2,147,483,648 to +2,147,483,647, the data element for the protect, unprotect, or reprotect APIs should be an 4-byte integer token type. However, if the user uses 2-byte integer token type, the data protection operation will not be successful. For a Bulk call using the protect, unprotect, and reprotect APIs, the error code, 44, appears. For a single call using the protect, unprotect, and reprotect APIs, an exception will be thrown and the error message, 44, Content of input data is not valid appears.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Integer token.

Table: Supported input data types for Big Data protectors with Integer token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Bytes as input that are not generated from string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes should be passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Integer token.

Table: Supported input data types for Data Warehouse protectors with Integer token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.3 - Credit Card

The Credit Card token type helps maintain transparency. It provides ways to clearly distinguish a token from the real value which is a recommendation of the PCI DSS. The Credit Card token type supports only numeric input (no separators are allowed as input).

Table: Credit Card Tokenization properties

The credit card number real value is distinguished from the tokenized value based on the token value validation properties.

Table: Specific Properties of the Credit Card Token Type

You can create a Credit Card token element and select no validation property for it. If the Credit Card token is involved, it will be handled similar to a Numeric token. However, additional checks will be applied to the input based on the properties detailed in the Credit Card token general properties column in the table above.

To enable the Credit Card token properties, such as, Invalid LUHN checksum and Invalid Card Type, with the SLT Tokenizers, refer to Credit Card Properties with SLT Tokenizers.

Invalid Luhn Checksum

The purpose of the Luhn checksum is to detect incorrectly entered card details. If you enable Invalid Luhn Checksum token validation, then you must use valid credit cards otherwise tokenization will be denied for an invalid credit card number.

A valid credit card has a valid Luhn checksum. Upon tokenization, the tokenized value will have an invalid Luhn checksum. Here is an example of the tokenized credit card with the invalid Luhn digit.

Table: Credit Card Number with Luhn Checksum Examples

Invalid Card Type

An invalid credit card indicates an issue with the credit card details. An invalid card type will result in token values not starting with the digits that real credit card numbers begin with. The first digit in a real credit card number is the Major Industry Identifier. Thus, digits 3,4,5,6, and 0 can be the first digits of the real credit card number, which are then substituted during tokenization.

Table: Real Credit Card Values with Tokenized Values

Here is an example of the tokenized credit card with the invalid card type.

Table: Credit Card Number with Invalid Card Type Examples

Alphabetic Indicator

The alphabetic indicator replaces the tokenized value with an alphabet. If you enable Alphabetic Indicator validation, then the resulting token value will have one alphabetic character.

You will need to choose the position of the alphabetic character before tokenizing a credit card number otherwise the resulting token will have no alphabetic indicator.

The alphabetic indicator will substitute the tokenized value according to the following rule:

Table: Alphabetic Indicator with Tokenized Digits

In the following table, the Visa Card Number “4067604564321454” is tokenized. A tokenized value, represented by “7594107411315001”, is substituted with an alphabetic character in a selected position.

Table: Examples of Credit Card Tokenization with Alphabetic Indicator

Credit Card Properties with SLT Tokenizers

The Credit Card Properties with SLT Tokenizers explains the minimum data length required for tokenization. This occurs when the Credit Card token properties is used in combination with the SLT Tokenizers.

If you enable Credit Card token properties for tokenization, such as Invalid LUHN checksum and Invalid Card Type, you need to select an appropriate SLT Tokenizer. This is required to ensure the minimum data length is available for successful tokenization.

The following table represents the minimum data length required for tokenization as per the usage of Credit Card token properties with the SLT Tokenizers.

Table: Minimum Data Length - Credit Card Token Properties with SLT Tokenizers

Credit Card Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Credit Card token.

Table: Supported input data types for Application protectors with Credit Card token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protector only supports bytes converted from the string data type. If any other data type is directly converted to bytes and passed as input to the Application Protectors APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Credit Card token.

Table: Supported input data types for Big Data protectors with Credit Card token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Bytes as input that are not generated from string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes should be passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Credit Card token.

Table: Supported input data types for Data Warehouse protectors with Credit Card token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.4 - Alpha (A-Z)

The Alpha token type tokenizes both uppercase and lowercase letters.

Table: Alpha Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Alpha token.

Table: Examples of Numeric tokenization values

Alpha Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Alpha token.

Note: For both SLT_1_3 and SLT_2_3, the maximum length of the protected data is 4096 bytes. This occurs for the Alpha token element for Application Protector with no length preservation.

Table: Supported input data types for Application protectors with Alpha token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protector only supports bytes converted from the string data type. If any other data type is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Alpha token.

Table: Supported input data types for Big Data protectors with Alpha token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2– The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data that is not converted to bytes from string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

^*3 – If you are using the Char tokenization UDFs in Hive, then ensure that the data elements have length preservation selected. In Char tokenization UDFs, using data elements without length preservation selected, is not supported.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Alpha token.

Table: Supported input data types for Data Warehouse protectors with Alpha token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.5 - Upper-Case Alpha (A-Z)

The Upper-Case Alpha token type tokenizes all alphabetic symbols as uppercase. After de-tokenization, all alphabetic symbols are returned as uppercase. This means that initial and detokenized values would not match if the input contains lowercase letters.

Table: Upper-Case Alpha Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Upper-case Alpha token.

Table: Examples of Upper Case Alpha tokenization values

Upper-case Alpha Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Upper-case Alpha token.

Table: Supported input data types for Application protectors with Upper-case Alpha token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Upper-Case Alpha token.

Table: Supported input data types for Big Data protectors with Upper-Case Alpha token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

^*3 – If you are using the Char tokenization UDFs in Hive, then ensure that the data elements have length preservation selected. In Char tokenization UDFs, using data elements without length preservation selected, is not supported.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Upper-case Alpha token.

Table: Supported input data types for Data Warehouse protectors with Upper-case Alpha token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.6 - Alpha-Numeric (0-9, a-z, A-Z)

The Alpha-numeric token type tokenizes all alphabetic symbols, including lowercase and uppercase letters. It also tokenizes digits from 0 to 9.

Table: Alpha-Numeric Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Alpha-Numeric token.

Table: Examples of Tokenization for Alpha-Numeric Values

Alpha-Numeric Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Alpha-Numeric token.

Table: Supported input data types for Application protectors with Alpha-Numeric token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Alpha-Numeric token.

Table: Supported input data types for Big Data protectors with Alpha-Numeric token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

^*3 – If you are using the Char tokenization UDFs in Hive, then ensure that the data elements have length preservation selected. In Char tokenization UDFs, using data elements without length preservation selected, is not supported.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Alpha-Numeric token.

Table: Supported input data types for Data Warehouse protectors with Alpha-Numeric token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.7 - Upper-Case Alpha-Numeric (0-9, A-Z)

The Upper-Case Alpha-Numeric token type tokenizes uppercase letters A through Z and digits 0 to 9. It tokenizes all alphabetic symbols as uppercase. After de-tokenization, all alphabetic symbols are returned as uppercase. This means that initial and detokenized values would not match if the input contains lowercase letters.

Table: Upper-Case Alpha-Numeric Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Upper-Case Alpha-Numeric token.

Table: Examples of Tokenization for Upper-Case Alpha-Numeric Values

Upper-Case Alpha-Numeric Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Upper-Case Alpha-Numeric token.

Table: Supported input data types for Application protectors with Upper-Case Alpha-Numeric token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Upper-Case Alpha-Numeric token.

Table: Supported input data types for Big Data protectors with Upper-Case Alpha-Numeric token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

^*3 – If you are using the Char tokenization UDFs in Hive, then ensure that the data elements have length preservation selected. In Char tokenization UDFs, using data elements without length preservation selected, is not supported.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Upper-Case Alpha-Numeric token.

Table: Supported input data types for Data Warehouse protectors with Upper-Case Alpha-Numeric token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.8 - Lower ASCII

The Lower ASCII token type is used to tokenize printable ASCII characters.

Table: Lower ASCII Tokenization Type properties

The following table shows examples of the way in which a value will be tokenized with the Lower ASCII token.

Table: Examples of Tokenization for Lower ASCII Values

Lower ASCII Tokenization Properties for different protectors

Lower ASCII tokenization should not be used with JSON or XML UDFs.

Application Protector

The following table shows supported input data types for Application protectors with the Lower ASCII token.

Table: Supported input data types for Application protectors with Lower ASCII token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Lower ASCII token.

Table: Supported input data types for Big Data protectors with Lower ASCII token

^*1 – If the input and output types of the API are BYTE[], then the customer application should convert the input to and output from the byte array, before calling the API.

^*2 – Ensure that you use the Horizontal tab “\t” as the field or column delimiter when loading data that is tokenized using Lower ASCII tokens for Hive, Pig, Impala, and Trino.

^*3 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Lower ASCII token.

Table: Supported input data types for Data Warehouse protectors with Lower ASCII token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.9 - Datetime (YYYY-MM-DD HH:MM:SS)

The Datetime token type was introduced in response to requirements to allow specific date parts to remain in the clear and for date tokens to be distinguishable from real dates. The Datetime token type allows time to be tokenized (HH:MM:SS) in fractions of a second, including milliseconds (MMM), microseconds (mmmmmm), and nanoseconds (nnnnnnnnn).

Table: Datetime Tokenization Type properties

The Tokenize Time property defines whether the time part (HH:MM:SS) will be tokenized. If Tokenize Time is set to “No”, the time part will be treated as a delimiter. It will be added to the date after tokenization.

The Distinguishable Date property defines whether the tokenized values will be outside of the normal date range.

If the Distinguishable Date option is enabled, then all tokenized dates will be in the range from year 5596-09-06 to 8334-08-03. The tokenized value will become recognizable. As an example, tokenizing “2012-04-25” can result in “6457-07-12”, which is distinguishable.

If the Distinguishable Date option is disabled, then the tokenized dates will be in the range from year 0600-01-01 to 3337-11-27. As an example, tokenizing “2012-04-25” will result in “1856-12-03”, which is non-distinguishable.

The Date in Clear property defines whether Month or Year will be left in the clear in the tokenized value.

Note: You cannot use enabled Distinguishable Date and select month or year to be left in the clear at the same time.

The following points are applicable when you tokenize the Dates with Year as 3337 by setting the Year part to be in clear:

• The tokenized Date value can be outside of the accepted Date range.
• The tokenized Date value can be de-tokenized to obtain the original Date value.

For example, if the Date 3337-11-27 is tokenized by setting the Year part 3337 in clear, then the resultant tokenized value 3337-12-15 is outside of the accepted Date range. The detokenization of this tokenized value returns the original Date 3337-11-27.

The following table shows examples of the way in which a value will be tokenized with the Datetime token.

Table: Examples of Tokenization for DateTime Values

Datetime Tokenization for Cutover Dates of the Proleptic Gregorian Calendar
The data systems, such as, Oracle or Java-based systems, do not accept the cutover dates of the Proleptic Gregorian Calendar. The cutover dates of the Proleptic Gregorian Calendar fall in the interval 1582-10-05 to 1582-10-14. These dates are converted to 1582-10-15. When using Oracle, conversion occurs by adding ten days to the source date. Due to this conversion, data loss occurs as the system is not capable to return the actual date value after the de-tokenization.

Note: The tokenization of the Date values in the cutover Date range of the Proleptic Gregorian Calendar results in an “Invalid Input” error.

The following points are applicable when the Distinguishable Date option is disabled:

• If the Distinguishable Date option is disabled, then the tokenized dates are in the range 0600-01-01 to 3337-11-27, which also includes the cutover date range. During tokenization, an internal validation is performed to check whether the value is tokenized to the cutover date. If it is a cutover date, then the Year part (1582) of the tokenized value is converted to 3338 and then returned.
• During de-tokenization, an internal check is performed to validate whether the Year is 3338. If the Year is 3338, then it is internally converted to 1582.

The following points are applicable when you tokenize the dates from the Year 1582 by setting the Year part to be in clear:

• The tokenized value can result in the cutover Date range. In such a scenario, the Year part of the tokenized Date value is converted to 3338.
• During de-tokenization, the Year part of the Date value is converted to 1582 to obtain the original date value.

For example, if the date 1582.04.30 12:12:12 is tokenized by setting the Year part in clear and the resultant tokenized value falls in the cutover Date range, then the Year part is converted to 3338 resulting in a tokenized value as 3338.10.10 12:12:12. The de-tokenization of this tokenized value returns the original Date 1582.04.30 12:12:12.

Note:
The tokenization accepts the date range 0600-01-01 to 3337-11-27 excluding the cutover date range.
The de-tokenization accepts the date range 0600-01-01 to 3337-11-27 and date values from the Year 3338. The year 3338 is accepted due to our support for tokenized value from the cutover date range.

Consider a scenario where you are migrating the protected data from Protector 1 to Protector 2. The Protector 1 includes the Datetime tokenizer update to process the cutover dates of the Proleptic Gregorian Calendar as input. The Protector 2 does not include this update. In such a scenario, an “Invalid Date Format” error occurs in Protector 2, when you try to unprotect the protected data as it fails to accept the input year 3338. The following steps must be performed to mitigate this issue:

1. Unprotect the protected data from Protector 1.
2. Migrate the unprotected data to Protector 2.
3. Protect the data from Protector 2.

Time zone Normalization for Datetime Tokens
The Datetime tokenizer does not normalize the timestamp with respect to the timezone before protecting the data.

In a few Protectors, the timezone normalization is done by the APIs that are used by the Protectors to retrieve the timestamp. However, this behavior can also be configured.

There are differences in handling timestamps. Therefore, you cannot rely on Datetime tokens for migration or transfer to different systems or timezones.

So, before migrating the Datetime tokens, ensure that the timestamps are normalized for timezones so that unprotecting the token value returns the original expected value.

Datetime Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Datetime token.

Table: Supported input data types for Application protectors with Datetime token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Datetime token.

Table: Supported input data types for Big Data protectors with Datetime token

^*1 – If the input and output types of the API are BYTE [], the customer application should convert the input to a byte array. Then, call the API and convert the output from the byte array.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Datetime token.

Table: Supported input data types for Data Warehouse protectors with Datetime token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.

1.1.4.10 - Decimal

The Decimal token type tokenizes numbers which may have a precision and scale. The resulting token does not contain any zeros which makes it suitable to store in a decimal data type in a database. Any sign or decimal point delimiter are stripped from the input value before tokenization and put back after tokenization.

Note: When data with decimal point delimiter is protected, the number of digits counted after the decimal point are length preserving. For example, consider decimal data “345645.345” is protected to return the protected value as “8638714.842”. The number of digits that exist after the decimal point remain the same in both the values.

Table: Decimal Tokenization Type properties

^*1 – The configurable input length for decimal values is between 1 and 36 digits. The upper range is 38 digits. However, since decimal token is not length preserving, only up to 36 digits are supported. Separators and sign characters are included in the length calculation.

Note: If you set custom maximum length for decimal token, then take into account that the actual maximum length of the input value should be 1-2 characters less than custom maximum. This type of token is non-length preserving, and the tokenized value can be 1-2 characters longer than the input value.

The following table shows examples of the way in which a value will be tokenized with the Decimal token.

Table: Examples of Tokenization for Decimal Values

Decimal Tokenization Properties for different protectors

Application Protector

The following table shows supported input data types for Application protectors with the Decimal token.

Table: Supported input data types for Application protectors with Decimal token

^*1 - The API accepts and returns data in BYTE[] format. The customer application needs to convert the input into byte arrays before calling the API, and similarly, convert the output from byte arrays after receiving the response from the API.

^*2 - The Protegrity Application Protectors only support bytes converted from the string data type. If int, short, or long format data is directly converted to bytes and passed as input to the Application Protector APIs that support byte as input and provide byte as output, then data corruption might occur.

For more information about Application protectors, refer to Application Protector.

Big Data Protector

Protegrity supports MapReduce, Hive, Pig, HBase, Spark, and Impala, which utilizes Hadoop Distributed File System (HDFS) or Ozone as the data storage layer. The data is protected from internal and external threats, and users and business processes can continue to utilize the secured data. Protegrity protects data inside the files using tokenization and strong encryption protection methods.

The following table shows supported input data types for Big Data protectors with the Decimal token.

Table: Supported input data types for Big Data protectors with Decimal token

^*1 – If the input and output types of the API are BYTE [], the customer application should convert the input to a byte array. Then, call the API and convert the output from the byte array.

^*2 – The Protegrity MapReduce protector, HBase coprocessor, and Spark protector only support bytes converted from the string data type. Data types that are not bytes converted from the string data type might cause data corruption to occur when:

• Any other data type is directly converted to bytes and passed as input to the MapReduce or Spark API that supports byte as input and provides byte as output.
• Any other data type is directly converted to bytes and inserted in an HBase table. Where the HBase table is configured with the Protegrity HBase coprocessor.

For more information about Big Data protectors, refer to Big Data Protector.

Data Warehouse Protector

The Protegrity Data Warehouse Protector is an advanced security solution designed to protect sensitive data at the column level. This enables you to secure your data, while still permitting access to authorized users. Additionally, the Data Warehouse Protector integrates seamlessly with existing database systems using the User-Defined Functions for an enhanced security. Protegrity protects data inside the data warehouses using various tokenization and encryption methods.

The following table shows the supported input data types for the Teradata protector with the Decimal token.

Table: Supported input data types for Data Warehouse protectors with Decimal token

For more information about Data Warehouse protectors, refer to Data Warehouse Protector.

Database Protectors

Oracle Database Protector

The supported input data types for the Oracle Database Protector are listed below.