Private Rank Aggregation under Local Differential Privacy

In typical collective decision-making scenarios, rank aggregation aims to combine different agents' preferences over the given alternatives into an aggregate ranking that agrees the most with all the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. All existing works that guarantee differential privacy in rank aggregation assume that the data curator is trusted. In this paper, we first formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response technique and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results demonstrate that the solution LDP-KwikSort:RR can achieve the practical trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.