3 papers
cs.CR2025
Bayes-Nash Generative Privacy Against Membership Inference Attacks
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
Membership inference attacks (MIAs) pose significant privacy risks by determining whether individual data is in a dataset. While differential privacy (DP) mitigates these risks, it…
cs.LG2025
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than dat…
cs.CR2025
Differential Confounding Privacy and Inverse Composition
Tao Zhang, Bradley A. Malin, Netanel Raviv +1
Differential privacy (DP) has become the gold standard for privacy-preserving data analysis, but its applicability can be limited in scenarios involving complex dependencies betwee…