Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy

arXiv:2402.10473v1 Announce Type: cross
Abstract: As machine learning (ML) becomes more prevalent in human-centric applications, there is a growing emphasis on algorithmic fairness and privacy protection. While previous research has explored these areas as separate objectives, there is a growing recognition of the complex relationship between privacy and fairness. However, previous works have primarily focused on examining the interplay between privacy and fairness through empirical investigations, with limited attention given to theoretical exploration. This study aims to bridge this gap …

applications arxiv cs.cr cs.it cs.lg differential privacy fair fairness human information local machine machine learning math.it obfuscation objectives prevalent privacy protection recognition relationship representation research