Problem
Research questions and friction points this paper is trying to address.
Privacy Risks
Synthetic Data
Local Combination Methods
Neighborhood Watch: Privacy Risks in Seeded Local Combination Synthetic Data
🤖 AI Summary
本文研究了合成数据生成方法中的隐私风险，特别是SMOTE、Simulant和Avatar三种局部组合方法，并通过多种攻击测试揭示了这些方法存在显著的隐私泄露问题。
📝 Abstract
Synthetic data is seen as a promising solution for sharing data in sensitive contexts. However, recent work on privacy attacks have shown that there are still significant residual risks, especially for synthetic data generations methods that are not based on formal approaches such as differential privacy. In this paper, we investigate the privacy risks associated with local combination approaches for generating synthetic data in which synthetic profiles are built by combining real neighbouring profiles. More precisely, we focus on three methods from this family, namely SMOTE, Simulant and Avatar, which have been recently used as a way to share 'anonymised data' in the healthcare domain. In particular, we conduct an extensive privacy analysis through a diverse set of attacks: membership inference, linkage and reconstruction attacks. Our results demonstrate substantial privacy leakage for all three methods, raising serious doubts about whether their outputs should be regarded as anonymous in practice.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Privacy Risks
Synthetic Data
Local Combination Methods
Membership Inference
Linkage Attacks
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