Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance
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Abstract (arXiv)
In this paper, we argue that similarity-based privacy metrics cannot ensure regulatory compliance of synthetic data. Our analysis and counter-examples show that they do not protect against singling out and linkability and, among other fundamental issues, completely ignore the motivated intruder test.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 24 Jul 2024.
Builds on
19 of 43 references.
References · 19
- 2019Modeling Tabular data using Conditional GANcited by 695
- 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
- 2022Language Models are Realistic Tabular Data Generatorscited by 290
- 2018Differentially Private Generative Adversarial Networkcited by 235
- 2020Auditing Differentially Private Machine Learning: How Private is Private SGD?cited by 213
- 2018Data Synthesis based on Generative Adversarial Networkscited by 184
- 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Spacecited by 183
- 2017Machine Learning Models that Remember Too Muchcited by 165
- 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
- 2021Winning the NIST Contest: A scalable and general approach to differentially private synthetic datacited by 101
- 2022TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Datacited by 56
- 2022Geographic Spines in the 2020 Census Disclosure Avoidance Systemcited by 42
7 more
- 2024"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generationcited by 20
- 2021Fidelity and Privacy of Synthetic Medical Datacited by 16
- 2024Differentially Private Release of Israel's National Registry of Live Birthscited by 16
- 2021Bounding Information Leakage in Machine Learningcited by 11
- 2023Synthetic Data: Methods, Use Cases, and Riskscited by 10
- 2022A Framework for Auditable Synthetic Data Generationcited by 7
- 2023When Synthetic Data Met Regulationcited by 3
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Citing papers per year: 2026: 1.
1 paper.