2 citations · 2 across the 5 of their papers we have counts for
6 papers
Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models
Boming Miao, Tao Zhang, Netanel Raviv +3
Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as diffusion models trained or a…
Sliced Rényi Pufferfish Privacy: Directional Additive Noise Mechanism and Private Learning with Gradient Clipping
Tao Zhang, Yevgeniy Vorobeychik
We study the design of a privatization mechanism and privacy accounting in the Pufferfish Privacy (PP) family. Specifically, motivated by the curse of dimensionality and lack of pr…
Residual-PAC Privacy: Automatic Privacy Control Beyond the Gaussian Barrier
Tao Zhang, Yevgeniy Vorobeychik
The Probably Approximately Correct (PAC) Privacy framework [46] provides a powerful instance-based methodology to preserve privacy in complex data-driven systems. Existing PAC Priv…
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…
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…
A Game-Theoretic Approach to Privacy-Utility Tradeoff in Sharing Genomic Summary Statistics
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
The advent of online genomic data-sharing services has sought to enhance the accessibility of large genomic datasets by allowing queries about genetic variants, such as summary sta…