Fairly Private Through Group Tagging and Relation Impact

TL;DR

This paper presents a novel privacy-preserving and fair classification architecture that balances privacy, utility, and fairness using group tagging, iterative shuffling, and relation impact, demonstrated through a gender equality case study.

Contribution

Introduces a new architecture combining group tagging, iterative shuffling, and relation impact for fair and private classification with differential privacy guarantees.

Findings

Achieves group fairness and optimal privacy-utility trade-off.

Effectively prevents linkage attacks through random shuffling.

Demonstrates effectiveness in a gender equality admission case study.

Abstract

Privacy and Fairness both are very important nowadays. For most of the cases in the online service providing system, users have to share their personal information with the organizations. In return, the clients not only demand a high privacy guarantee to their sensitive data but also expected to be treated fairly irrespective of their age, gender, religion, race, skin color, or other sensitive protected attributes. Our work introduces a novel architecture that is balanced among the privacy-utility-fairness trade-off. The proposed mechanism applies Group Tagging Method and Fairly Iterative Shuffling (FIS) that amplifies privacy through random shuffling and prevents linkage attack. The algorithm introduces a fair classification problem by Relation Impact based on Equalized Minimal FPR-FNR among the protected tagged group. For the count report generation, the aggregator uses TF-IDF to add noise for providing longitudinal Differential Privacy guarantee. Lastly, the mechanism boosts the utility through risk minimization function and obtain the optimal privacy-utility budget of the system. In our work, we have done a case study on gender equality in the admission system and helps to obtain a satisfying result which implies that the proposed architecture achieves the group fairness and optimal privacy-utility trade-off for both the numerical and decision making Queries.