Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
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DOI：
10.24963/ijcai.2022/766
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Ferdinando Fioretto;Cuong Tran;P. V. Hentenryck;Keyu Zhu
中科院分区：
文献类型：
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作者：
Ferdinando Fioretto;Cuong Tran;P. V. Hentenryck;Keyu Zhu
本文综述了差分隐私（DP）与公平交叉领域的最新研究成果。它侧重于调查工作，观察到DP系统可能会加剧偏见和不同群体的个体的不同影响。该调查回顾了隐私和公平可能一致或对比目标的条件，分析了DP如何以及为什么加剧了决策问题和学习任务中的偏见和不公平，并回顾了缓解DP系统中出现的公平问题的可用解决方案。该调查提供了对在公平视角下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的统一理解。
This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the conditions under which privacy and fairness may be aligned or contrasting goals, analyzes how and why DP exacerbates bias and unfairness in decision problems and learning tasks, and reviews the available solutions to mitigate 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.