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.

翻译：本研究探讨如何优化设计局部差分隐私机制，以降低数据不公平性，从而提升下游分类任务的公平性。我们首先针对二元敏感属性推导出闭式最优机制，继而构建可处理的优化框架，为多值属性生成相应的最优机制。在理论贡献方面，我们证明对于判别精度最优的分类器，降低数据不公平性必然导致分类不公平性的减少，从而建立了隐私感知预处理与分类公平性之间的直接关联。实证研究表明，在多种数据集和公平性指标下，我们的方法在降低数据不公平性方面持续优于现有LDP机制，同时保持与非隐私模型相近的精度。此外，与主流的预处理和后处理公平性方法相比，本机制在实现更优的精度-公平性权衡的同时，有效保护了敏感属性的隐私。综合而言，这些结果凸显了LDP作为一种原理清晰且有效的预处理公平性干预技术的优势。