Differentially Private Rankings via Outranking Methods and Performance Data Aggregation

TL;DR

This paper presents a novel method combining Multi-Criteria Decision Making with Differential Privacy to protect user data in ranking systems, ensuring privacy without significantly compromising ranking accuracy.

Contribution

It introduces an integrated approach that applies Differential Privacy to MCDM outranking methods using data aggregation, addressing privacy concerns in sensitive decision-making contexts.

Findings

Strong correlation between true and anonymized rankings

Effective privacy guarantees with robust statistical support

Applicable to dynamic, data-driven decision systems

Abstract

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.