Privacy-Enhancing Technologies in Zero-Trust Architecture

Privacy-Enhancing Technologies in Zero-Trust Architecture

Privacy-enhancing technologies (PETs) complement zero-trust principles by enabling data utility while protecting privacy. These technologies become essential as organizations balance analytical needs with privacy requirements. Federated learning trains machine learning models across distributed data without centralizing sensitive information. Secure enclaves process sensitive data in isolated, encrypted environments.

Synthetic data generation creates realistic datasets for development and testing without exposing actual user data. Advanced techniques ensure synthetic data maintains statistical properties while preventing re-identification. This approach enables realistic testing and development while completely eliminating privacy risks. Differential privacy budgets track privacy loss across multiple queries, preventing adversaries from reconstructing individual records through repeated analysis.