Privacy-Preserving Characteristics

No Direct Link to Real Individuals

Protegrity Synthetic Data is generated from learned patterns in real datasets but does not contain any actual personal records. This ensures:

• No 1:1 mapping between synthetic and real data.
• No re-identification risk, even when used in sensitive domains, such as healthcare or finance.

Compliance with Privacy Regulations

• General Data Protection Regulation (GDPR): Synthetic Data is considered anonymous under GDPR. It lacks identifiable links to real individuals.
• Health Insurance Portability and Accountability Act (HIPAA): It qualifies under Safe Harbor and Expert Determination methods. This makes it suitable for healthcare data use, without being classified as Protected Health Information (PHI).

Built-In Privacy Safeguards

Protegrity’s Synthetic Data solution includes multiple privacy-enhancing features:

• Privacy Measurement Tools: It evaluates the robustness of data.
• Automated De-identification: It removes sensitive attributes while preserving data utility.
• Support for Tabular Data: It enables realistic simulation of structured datasets for analytics and AI training.
• On-demand Generation Capabilities: It allows developers to invoke Synthetic Data generation using API and integrate it into pipelines with minimal effort.