# Private Rank Aggregation in Central and Local Models

arXiv: https://arxiv.org/abs/2112.14652 · HTML: https://mlsanity.com/paper/2112.14652/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [Better Private Algorithms for Correlation Clustering](https://mlsanity.com/paper/2202.10747/) (2022)
- [Ranking Differential Privacy](https://mlsanity.com/paper/2301.00841/) (2023)
- [Rate-Optimal Rank Aggregation with Private Pairwise Rankings](https://mlsanity.com/paper/2402.16792/) (2024)
- [Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding](https://mlsanity.com/paper/2409.00848/) (2024)

## Abstract (arXiv)

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.