Private Rank Aggregation under Local Differential Privacy
This addresses privacy concerns in crowdsourced data management for agents with untrusted curators, representing an incremental advance by adapting existing methods to a new trust model.
The paper tackles the problem of rank aggregation under local differential privacy, where agents do not trust the data curator, by proposing the LDP-KwikSort protocol. The results show it achieves an acceptable trade-off between utility and privacy protection for agents' pairwise preferences.
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.