Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead

• URL: http://arxiv.org/abs/2503.20846v1
• Date: Wed, 26 Mar 2025 16:06:33 GMT
• Title: Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
• Authors: Viktor Schlegel, Anil A Bharath, Zilong Zhao, Kevin Yee,
• Abstract summary: Privacy-preserving synthetic data offers a promising solution to harness segregated data in high-stakes domains.<n>This survey presents the theoretical foundations of generative models and differential privacy followed by a review of state-of-the-art methods.<n>Our findings highlight key challenges including unaccounted privacy leakage, insufficient empirical verification of formal guarantees, and a critical deficit of realistic benchmarks.
• Score: 7.410975558116122
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Privacy-preserving synthetic data offers a promising solution to harness segregated data in high-stakes domains where information is compartmentalized for regulatory, privacy, or institutional reasons. This survey provides a comprehensive framework for understanding the landscape of privacy-preserving synthetic data, presenting the theoretical foundations of generative models and differential privacy followed by a review of state-of-the-art methods across tabular data, images, and text. Our synthesis of evaluation approaches highlights the fundamental trade-off between utility for down-stream tasks and privacy guarantees, while identifying critical research gaps: the lack of realistic benchmarks representing specialized domains and insufficient empirical evaluations required to contextualise formal guarantees. Through empirical analysis of four leading methods on five real-world datasets from specialized domains, we demonstrate significant performance degradation under realistic privacy constraints ($\epsilon \leq 4$), revealing a substantial gap between results reported on general domain benchmarks and performance on domain-specific data. %Our findings highlight key challenges including unaccounted privacy leakage, insufficient empirical verification of formal guarantees, and a critical deficit of realistic benchmarks. These challenges underscore the need for robust evaluation frameworks, standardized benchmarks for specialized domains, and improved techniques to address the unique requirements of privacy-sensitive fields such that this technology can deliver on its considerable potential.

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