Private Rank Aggregation in Central and Local Models
Data Structures and Algorithms
2021-12-30 v1 Cryptography and Security
Computer Science and Game Theory
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.
Cite
@article{arxiv.2112.14652,
title = {Private Rank Aggregation in Central and Local Models},
author = {Daniel Alabi and Badih Ghazi and Ravi Kumar and Pasin Manurangsi},
journal= {arXiv preprint arXiv:2112.14652},
year = {2021}
}
Comments
To appear in the Proceedings of the 2022 AAAI Conference on Artificial Intelligence