  As a method for answer aggregation in crowdsourced data management, rank
aggregation aims to combine different agents' answers or preferences over the
given alternatives into an aggregate ranking which agrees the most with 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. Existing works that guarantee differential privacy in
rank aggregation all assume that the data curator is trusted. In this paper, we
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 mechanism, and the Laplace mechanism, we propose an effective and
efficient protocol LDP-KwikSort. Theoretical and empirical results show that
the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the
utility of aggregate ranking and the privacy protection of agents' pairwise
preferences.
