# The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy

arXiv: https://arxiv.org/abs/1409.6831 · HTML: https://mlsanity.com/paper/1409.6831/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2014.

## Built on by (most cited first, 6 of 6)

- [Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning](https://mlsanity.com/paper/2303.07152/) (2023)
- [Differentially Private Condorcet Voting](https://mlsanity.com/paper/2206.13081/) (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)
- [Optimal Differentially Private Ranking from Pairwise Comparisons](https://mlsanity.com/paper/2507.09388/) (2025)
- [Differentially Private Rankings via Outranking Methods and Performance Data Aggregation](https://mlsanity.com/paper/2511.09120/) (2025)

## Abstract (arXiv)

The potential risk of privacy leakage prevents users from sharing their honest opinions on social platforms. This paper addresses the problem of privacy preservation if the query returns the histogram of rankings. The framework of differential privacy is applied to rank aggregation. The error probability of the aggregated ranking is analyzed as a result of noise added in order to achieve differential privacy. Upper bounds on the error rates for any positional ranking rule are derived under the assumption that profiles are uniformly distributed. Simulation results are provided to validate the probabilistic analysis.