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stat.ML2026
On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework
Ruyu Zhou, Fang Liu
Differential Privacy (DP) provides a rigorous framework for quantifying privacy guarantees. While most methods achieve DP by injecting calibrated random noise, the intrinsic random…
stat.ML2023
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)
Xingyuan Zhao, Ruyu Zhou, Fang Liu
Applying a randomized algorithm to a subset rather than the entire dataset amplifies privacy guarantees. We propose a class of subsampling methods ``MUltistage Sampling Technique (…