论文标题
差异私人数据的后处理：公平的观点
Post-processing of Differentially Private Data: A Fairness Perspective
论文作者
论文摘要
后处理的免疫力是具有差异隐私的基本属性：它可以使数据独立转换对差异化私人产出而不会影响其隐私保证。后处理通常用于数据释放应用程序，包括人口普查数据，然后将其用于对具有重大社会影响的分配。本文表明，后处理会对个人或群体产生不同的影响，并分析两个关键环境：释放差异私人数据集以及使用此类私人数据集用于下游决策，例如美国人口普查数据提供的资金分配。在第一个环境中，本文提出了有关传统后处理机制不公平性的紧密界限，为决策者提供了独特的工具，以量化其发布所带来的不同影响。在第二个环境中，本文提出了一种新颖的后处理机制，该机制（大约）在不同的公平指标下是最佳的，可以大大减少公平性问题或降低隐私成本。理论分析与人口普查数据的数值模拟相辅相成。
Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees. Post-processing is routinely applied in data-release applications, including census data, which are then used to make allocations with substantial societal impacts. This paper shows that post-processing causes disparate impacts on individuals or groups and analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions, such as the allocation of funds informed by US Census data. In the first setting, the paper proposes tight bounds on the unfairness of traditional post-processing mechanisms, giving a unique tool to decision-makers to quantify the disparate impacts introduced by their release. In the second setting, this paper proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics, either reducing fairness issues substantially or reducing the cost of privacy. The theoretical analysis is complemented with numerical simulations on Census data.
