Private rank aggregation in central and local models.


Daniel Alabi, Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. 2022. “Private rank aggregation in central and local models.” In In Proceedings of the 2022 AAAI Conference on Artificial Intelligence. Publisher's Version
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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.
Last updated on 03/24/2022