Private Rank Aggregation in Central and Local Models

TL;DR

This paper develops differentially private algorithms for rank aggregation in social choice, providing utility bounds in both central and local privacy models, balancing privacy with accuracy.

Contribution

It introduces novel differentially private algorithms for rank aggregation and establishes utility bounds in both central and local privacy models.

Findings

Algorithms achieve privacy with bounded utility loss

Utility bounds are tight in the central model

Utility bounds are also established for the local model

Abstract

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.