# Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

Source: [Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano](<https://www.sota2.com/research/paper/68fa5ab21e209131ce9c205e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T16:38:49.818Z

Authors: Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi

Published: 2022-10-24T23:50:12.000Z

## Abstract

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 $\\epsilon$.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Membership Inference Advantage Bounding on Uniform prior size 10](<https://www.sota2.com/research/sota/membership-inference-advantage-bounding-on-uniform-prior-size-10.md>): Membership Inference Advantage Bounding; Uniform prior size 10; Advantage Upper Bound: 0.976.

- [Membership Inference Advantage Bounding on Uniform prior size 100](<https://www.sota2.com/research/sota/membership-inference-advantage-bounding-on-uniform-prior-size-100.md>): Membership Inference Advantage Bounding; Uniform prior size 100; Advantage Upper Bound: 4.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.13662>)

[PDF](<https://arxiv.org/pdf/2210.13662.pdf>)