Differentially Private Fair Binary Classifications

2024/02/23 by Hrad Ghoukasian, Shahab Asoodeh, Ghoukasian, Hrad +1 · 3 citations
Computer Science · Medicine · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Pharmacological Effects and Toxicity Studies #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2402.15603

openalex publication_date 2024/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we investigate binary classification under the constraints of both differential privacy and fairness. We first propose an algorithm based on the decoupling technique for learning a classifier with only fairness guarantee. This algorithm takes in classifiers trained on different demographic groups and generates a single classifier satisfying statistical parity. We then refine this algorithm to incorporate differential privacy. The performance of the final algorithm is rigorously examined in terms of privacy, fairness, and utility guarantees. Empirical evaluations conducted on the Adult and Credit Card datasets illustrate that our algorithm outperforms the state-of-the-art in terms of fairness guarantees, while maintaining the same level of privacy and utility.

Cited by

• An Information Geometric Approach to Fairness With Equalized Odds Constraint
• On Information Theoretic Fairness With A Bounded Point-Wise Statistical Parity Constraint: An Information Geometric Approach
• Information-Theoretic Fairness with A Bounded Statistical Parity Constraint

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