Differentially private histogram and synthetic data publication

Abstract

Differential privacy has recently emerged as one of the strongest privacy guarantees by making few assumptions on the background or external knowledge of an attacker. Differentially private data analysis and publishing have received considerable attention in biomedical communities as promising approaches for sharing medical and health data, while preserving the privacy of individuals represented in data records. In this chapter, we provide a broad survey of the recent works in differentially private histogram and synthetic data publishing. We categorize most recent and emerging techniques in this field from two major aspects: (a) various data types (e.g, relational data, transaction data, dynamic stream data, etc.), and (b) parametric, and non-parametric techniques. We also present some challenges and future research directions for releasing differentially private histogram and synthetic data in health and medical data.