Decision Making with Differential Privacy under a Fairness Lens

TL;DR

This paper examines how differential privacy affects fairness in decision-making processes, highlighting disproportionate impacts on groups and proposing mitigation strategies based on analysis of privacy-preserving data releases.

Contribution

It provides an analysis of fairness issues in differentially private data releases and offers guidelines to reduce bias in resource allocation tasks.

Findings

Differential privacy introduces disproportionate impacts on certain groups.

Analysis reveals reasons behind fairness disparities.

Proposed mitigation strategies improve fairness in decision outcomes.

Abstract

Agencies, such as the U.S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform to privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. {The paper shows that, when the decisions take as input differentially private data}, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.