Creating Protegrity Anonymization Requests

A general overview of the process you need to follow to anonymize the data is shown in the following figure:

1. Identify the dataset that needs to be anonymized.
2. Analyze and classify the various fields available in the dataset. The following classifications are available:
   • Direct Identifiers
   • Quasi-Identifier
   • Sensitive Attributes
   • Non-Sensitive Attributes
3. Determine the use case by specifying the data that is required for further analysis.
4. Specify the quasi-identifiers and other fields that are not required in the dataset
5. Specify the required Protegrity Anonymization methods for the data. Some commonly used methods are as follows:
   • Generalization
   • Micro-Aggregation
6. Specify and measure the acceptable statistics and risk levels for the data fields for measuring the statistic before running the Protegrity Anonymization job.

Note: For more information about different risk levels for the data fields, refer to Protegrity Anonymization models.

7. Verify that the anonymized data satisfies the acceptable risk threshold level.
8. Measure the quality of the anonymized data by comparing it with the original data. If the quality does not meet standards, then work on the data or drop the output.
9. Save the anonymized data to an output file.

The anonymized data can now be used for further analysis and as input for machine learning softwares.