# Differentially Private Rankings via Outranking Methods and Performance Data Aggregation

arXiv: https://arxiv.org/abs/2511.09120 · HTML: https://mlsanity.com/paper/2511.09120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees](https://mlsanity.com/paper/1612.02298/) (2016)
- [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)
- [Electre-Score: A first outranking based method for scoring actions](https://mlsanity.com/paper/1905.06089/) (2019)

## Abstract (arXiv)

Multiple-Criteria Decision Making (MCDM) is a sub-discipline of Operations Research that helps decision-makers in choosing, ranking, or sorting alternatives based on conflicting criteria. Over time, its application has been expanded into dynamic and data-driven domains, such as recommender systems. In these contexts, the availability and handling of personal and sensitive data can play a critical role in the decision-making process. Despite this increased reliance on sensitive data, the integration of privacy mechanisms with MCDM methods is underdeveloped. This paper introduces an integrated approach that combines MCDM outranking methods with Differential Privacy (DP), safeguarding individual contributions' privacy in ranking problems. This approach relies on a pre-processing step to aggregate multiple user evaluations into a comprehensive performance matrix. The evaluation results show a strong to very strong statistical correlation between the true rankings and their anonymized counterparts, ensuring robust privacy parameter guarantees.