Does Privacy Always Harm Fairness? Data-Dependent Trade-offs via Chernoff Information Neural Estimation

2026-01-20

AI Summary

This study investigates the intrinsic trade-offs among privacy, fairness, and accuracy, with a particular focus on whether privacy necessarily compromises fairness. To this end, the authors propose the “Noisy Chernoff Difference,” a novel metric grounded in Chernoff information, which captures the data-dependent nature of this triadic relationship and serves as a proxy for the steepness of the fairness–accuracy trade-off curve. Leveraging neural estimation and modeling on both synthetic and real-world datasets, the approach identifies three canonical behavioral patterns in synthetic data along with their corresponding distributional characteristics. Empirical validation on real datasets further demonstrates the method’s efficacy and reveals the conditional nature of how privacy mechanisms affect fairness—highlighting that the impact of privacy on fairness is not universal but depends critically on underlying data properties.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

Abstract

Fairness and privacy are two vital pillars of trustworthy machine learning. Despite extensive research on these individual topics, the relationship between fairness and privacy has received significantly less attention. In this paper, we utilize the information-theoretic measure Chernoff Information to highlight the data-dependent nature of the relationship among the triad of fairness, privacy, and accuracy. We first define Noisy Chernoff Difference, a tool that allows us to analyze the relationship among the triad simultaneously. We then show that for synthetic data, this value behaves in 3 distinct ways (depending on the distribution of the data). We highlight the data distributions involved in these cases and explore their fairness and privacy implications. Additionally, we show that Noisy Chernoff Difference acts as a proxy for the steepness of the fairness-accuracy curves. Finally, we propose a method for estimating Chernoff Information on data from unknown distributions and utilize this framework to examine the triad dynamic on real datasets. This work builds towards a unified understanding of the fairness-privacy-accuracy relationship and highlights its data-dependent nature.

Problem

• fairness
• privacy
• Chernoff Information
• data-dependent trade-offs
• accuracy

Innovation

• Chernoff Information
• Noisy Chernoff Difference
• fairness-privacy trade-off
• data-dependent analysis
• neural estimation