Differentially Private Rankings via Outranking Methods and Performance Data Aggregation
This addresses privacy concerns in data-driven domains like recommender systems for decision-makers, though it appears incremental as it integrates existing DP with MCDM methods.
The paper tackled the problem of integrating privacy mechanisms with Multiple-Criteria Decision Making (MCDM) methods in ranking problems, introducing an approach that combines MCDM outranking methods with Differential Privacy (DP) to safeguard individual contributions. The result showed a strong to very strong statistical correlation between true rankings and anonymized counterparts, ensuring robust privacy guarantees.
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.