Neighborhood Watch: Privacy Risks in Seeded Local Combination Synthetic Data
cs.CR
Submitted: 2026-08-27
Updated: 2026-08-27
Terminology
Sources
- SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
- A Unified Framework for Quantifying Privacy Risk in Synthetic Data
- LOGAN: Membership Inference Attacks Against Generative Models
- Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
- Membership Inference Attacks against Synthetic Data through Overfitting Detection
- Ensembling Membership Inference Attacks Against Tabular Generative Models
- Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis
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