# Ranking Differential Privacy

arXiv: https://arxiv.org/abs/2301.00841 · HTML: https://mlsanity.com/paper/2301.00841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12](https://mlsanity.com/paper/1709.02753/) (2017)
- [Cross-Batch Negative Sampling for Training Two-Tower Recommenders](https://mlsanity.com/paper/2110.15154/) (2021)
- [You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism](https://mlsanity.com/paper/2110.14802/) (2021)
- [Does Label Differential Privacy Prevent Label Inference Attacks?](https://mlsanity.com/paper/2202.12968/) (2022)
- [The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy](https://mlsanity.com/paper/1409.6831/) (2014)
- [Private Rank Aggregation under Local Differential Privacy](https://mlsanity.com/paper/1908.04486/) (2019)
- [Private Rank Aggregation in Central and Local Models](https://mlsanity.com/paper/2112.14652/) (2021)
- [Utility-Preserving Differentially Private Data Releases Via Individual Ranking Microaggregation](https://mlsanity.com/paper/1512.02897/) (2015)
- [Distributed Differentially Private Ranking Aggregation](https://mlsanity.com/paper/2202.03388/) (2022)
- [Differentially Private Condorcet Voting](https://mlsanity.com/paper/2206.13081/) (2022)
- [Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy](https://mlsanity.com/paper/1808.08181/) (2018)
- [A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review](https://mlsanity.com/paper/2201.02539/) (2022)

## Built on by (most cited first, 1 of 1)

- [Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning](https://mlsanity.com/paper/2303.07152/) (2023)

## Abstract (arXiv)

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the ε-differential privacy. This paper proposes a novel notion called ε-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed ε-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed ε-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how ε affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.