Privacy-preserving Approximate Query Processing with Differentially Private Generative Models

The increasing importance of data-driven decision-making across various sectors, coupled with the need for efficient large-scale data analysis while upholding privacy, prompts our exploration. Accordingly, approaches like synthetic data generation and approximate query processing have arisen. In this study, we combined differential privacy with approximate query processing for machine learning to enhance privacy. Our approach has been implemented through differentially private generative models within an approximate query processing framework, all of which safeguard data privacy. We provide assessments of synthetic data quality concerning sensitive data and the relative error in approximate query processing utilizing synthetic data.