Differentially Private Data Synthesis Using Variational Autoencoders

Abstract

This thesis presents a novel approach to differentially private data synthesis using variational autoencoders (VAEs). We develop a framework that generates synthetic data while preserving privacy guarantees through differential privacy mechanisms. Our approach combines the generative capabilities of VAEs with rigorous privacy protection, enabling the creation of useful synthetic datasets for research and analysis while maintaining individual privacy.