Distributed Differentially Private Ranking Aggregation

Abstract

A key step in cooperative decision-making is for all participants to achieve a consensus that avoids individual favoritism. To reach a consensus, a quantitative systematic mechanism is sometimes preferred. An example of such a mechanism is ranking aggregation, where the task is to rank elements in a certain order. While participating in ranking activities, it is also critical under certain circumstances to protect each decision maker’s preference. A promising privacy-preserving technique that is suitable for such a need is differential privacy (DP), which ensures plausible deniability of the protected information with rigorous mathematical guarantee and adjustable privacy level. A concern of the standard DP model is its assumption of letting a curator collect and analyze sensitive information, where in practical situations such a trusted independent curator may not exist. This article proposed a mechanism to solve the above issue using the distributed DP (DDP) framework. The proposed mechanism collects locally differential private rankings from individuals, and then randomly permutes pairwise rankings using a shuffle model to further amplify privacy protection. The final representative is produced by hierarchical rank aggregation (HRA). The mechanism was theoretically analyzed and experimentally compared against the existing methods and demonstrated competitive results in both output accuracy and privacy protection.