Computer Science > Machine Learning
[Submitted on 24 Oct 2022 (v1), last revised 10 Aug 2023 (this version, v2)]
Title:Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
View PDFAbstract:Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter $\epsilon$ needs to be to protect against certain privacy risks in practice is still not well-understood. In this work, we study data reconstruction attacks for discrete data and analyze it under the framework of multiple hypothesis testing. We utilize different variants of the celebrated Fano's inequality to derive upper bounds on the inferential power of a data reconstruction adversary when the model is trained differentially privately. Importantly, we show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $\epsilon$ can be $O(\log M)$ before the adversary gains significant inferential power. Our analysis offers theoretical evidence for the empirical effectiveness of DP against data reconstruction attacks even at relatively large values of $\epsilon$.
Submission history
From: Chuan Guo [view email][v1] Mon, 24 Oct 2022 23:50:12 UTC (823 KB)
[v2] Thu, 10 Aug 2023 03:02:21 UTC (858 KB)
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