In social choice theory, (Kemeny) rank aggregation is a well-studied problem where the goal is to combine rankings from multiple voters into a single ranking on the same set of items. Since rankings can reveal preferences of voters (which a voter might like to keep private), it is important to aggregate preferences in such a way to preserve privacy. In this work, we present differentially private algorithms for rank aggregation in the pure and approximate settings along with distribution-independent utility upper and lower bounds. In addition to bounds in the central model, we also present utility bounds for the local model of differential privacy.

翻译：在社会选择理论中,(凯梅尼)排名汇总是一个研究周密的问题,其目标是将多选民的排名合并成同一组项目的单一排名。由于排名可以显示选民的偏好(选民可能希望保持隐私),重要的是以这种保护隐私的方式汇总偏好。在这项工作中,我们提出了在纯度和近似环境中的排名汇总有差别的私人算法,以及分布独立的公用事业上下限。除了中央模式的界限外,我们还提出了差异隐私本地模式的实用界限。