Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data as well as the resulting decisions, several ethical concerns need to be addressed for the appropriate use of such technologies, in particular, fairness and privacy. Unlike previous work, which focused on centralized differential privacy (DP) or local DP (LDP) for a single sensitive attribute, in this paper, we examine the impact of LDP in the presence of several sensitive attributes (i.e., multi-dimensional data) on fairness. Detailed empirical analysis on synthetic and benchmark datasets revealed very relevant observations. In particular, (1) multi-dimensional LDP is an efficient approach to reduce disparity, (2) the multi-dimensional approach of LDP (independent vs. combined) matters only at low privacy guarantees, and (3) the outcome Y distribution has an important effect on which group is more sensitive to the obfuscation. Last, we summarize our findings in the form of recommendations to guide practitioners in adopting effective privacy-preserving practices while maintaining fairness and utility in ML applications.

翻译：自动决策系统越来越多地被用于影响人们生活的重大决策中。由于所处理数据的敏感性及其产生的决策结果，合理使用此类技术需要解决若干伦理问题，特别是公平性和隐私性。不同于以往关注单一敏感属性的集中式差分隐私或局部差分隐私的研究，本文探讨了在存在多个敏感属性（即多维数据）的情况下，局部差分隐私对公平性的影响。基于合成数据集和基准数据集的详细实证分析揭示了重要发现。具体而言：（1）多维局部差分隐私是减少偏差的有效方法；（2）局部差分隐私的多维处理方式（独立处理与联合处理）仅在低隐私保证级别时产生显著差异；（3）结果变量Y的分布对哪些群体对混淆更敏感有重要影响。最后，我们将研究结果总结为实践建议，以指导从业者在保持机器学习应用公平性和实用性的同时，采用有效的隐私保护实践。