Decision Making with Differential Privacy under a Fairness Lens
This addresses fairness issues in privacy-preserving data for agencies like the U.S. Census Bureau, though it is incremental as it builds on existing differential privacy methods.
The paper investigates how differentially private data releases disproportionately affect certain groups in resource allocation tasks, analyzing the causes and proposing mitigation guidelines.
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.