This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks, and describes available mitigation measures for the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine-learning or decisions-making tasks under a fairness lens.
翻译:本文件调查了不同隐私(DP)和公平之间最近开展的工作,审查了隐私和公平可能调整或对比目标的条件,分析了DP如何和为什么可能加剧决策问题和学习任务中的偏见和不公平,并介绍了DP系统产生的公平问题的现有缓解措施,调查从公平角度统一了解了在部署保护隐私的机器学习或决策任务时出现的主要挑战和潜在风险。