Privacy-Preserving Technologies

Privacy-Preserving Technologies

Privacy-preserving technologies enable data utility while protecting individual privacy. Homomorphic encryption allows computation on encrypted data without decryption. Secure multi-party computation enables collaborative analysis without sharing raw data. Differential privacy adds carefully calibrated noise to protect individual records while maintaining statistical accuracy. These technologies, once purely academic, now see practical deployment in production systems.

Federated learning trains machine learning models across distributed data without centralizing information. This approach enables organizations to benefit from collective intelligence while maintaining data sovereignty. Privacy-preserving analytics provide insights without exposing individual records. Synthetic data generation creates realistic datasets for development and testing without privacy risks.