提出最优本地差分隐私机制，兼顾隐私保护与分类公平性
Optimal Fairness under Local Differential Privacy
- 基于闭式解设计针对二元敏感属性的最优隐私机制
- 在多值属性下实现更低的数据不公平性，且保持接近非私有模型的准确率
- 首次建立隐私预处理与分类公平性的理论关联，适合关注隐私公平的科研人员
我们研究如何最优设计本地差分隐私（LDP）机制，以降低数据不公平性并提升下游分类的公平性。首先推导出二元敏感属性的闭式最优机制，并构建可计算的优化框架，得到多值属性对应的最优机制。理论上，我们证明对于判别-准确率最优的分类器，降低数据不公平性必然导致分类不公平性下降，从而建立了隐私感知预处理与分类公平之间的直接联系。实验表明，本方法在多种数据集和公平性度量下，持续优于现有LDP机制，显著减少数据不公平性，同时保持接近非私有模型的准确率。相比主流预处理与后处理公平方法，本机制在准确率-公平性权衡上表现更优，且有效保护敏感属性隐私。结果表明，LDP是一种原理严谨、高效的公平性预处理技术。
原文摘要 · Abstract (English)
We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first derive a closed-form optimal mechanism for binary sensitive attributes and then develop a tractable optimization framework that yields the corresponding optimal mechanism for multi-valued attributes. As a theoretical contribution, we establish that for discrimination-accuracy optimal classifiers, reducing data unfairness necessarily leads to lower classification unfairness, thus providing a direct link between privacy-aware pre-processing and classification fairness. Empirically, we demonstrate that our approach consistently outperforms existing LDP mechanisms in reducing data unfairness across diverse datasets and fairness metrics, while maintaining accuracy close to that of non-private models. Moreover, compared with leading pre-processing and post-processing fairness methods, our mechanism achieves a more favorable accuracy-fairness trade-off while simultaneously preserving the privacy of sensitive attributes. Taken together, these results highlight LDP as a principled and effective pre-processing fairness intervention technique.
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