Privacy-Preserving Machine Learning

6 Privacy-preserving synthetic data generation

This chapter covers

• Synthetic data generation
• Generating synthetic data for anonymization
• Using differential privacy mechanisms to generate privacy-preserving synthetic data
• Designing a privacy-preserving synthetic data generation scheme for machine learning tasks

So far we’ve looked into the concepts of differential privacy (including the centralized, DP, and the local, LDP, versions) and their applications in developing privacy-preserving query-processing and machine learning (ML) algorithms. As you saw, the idea of DP is to add noise to the query results (without disturbing their original properties) such that the results can assure the privacy of the individuals while satisfying the utility ...