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.

翻译：多准则决策（MCDM）是运筹学的一个分支学科，旨在帮助决策者基于相互冲突的准则对备选方案进行选择、排序或分类。随着时间推移，其应用已扩展到动态和数据驱动的领域，例如推荐系统。在这些场景中，个人敏感数据的可用性与处理方式可能在决策过程中起到关键作用。尽管对敏感数据的依赖日益增加，但隐私保护机制与MCDM方法的结合仍处于发展不足的状态。本文提出一种集成方法，将MCDM优劣排序法与差分隐私（DP）相结合，以保护排名问题中个体贡献的隐私。该方法通过预处理步骤将多用户评估聚合为综合性能矩阵。评估结果表明，真实排名与其匿名化版本之间存在强至极强的统计相关性，同时确保了稳健的隐私参数保障。