用差分隐私保护用户评分数据，实现安全可靠的排序推荐。
Differentially Private Rankings via Outranking Methods and Performance Data Aggregation
- 通过聚合用户评分构建性能矩阵，预处理阶段加入差分隐私
- 匿名化排名与真实排名相关性达强至极强，保障隐私不泄露
- 适合需要保护用户隐私的推荐系统、决策分析场景
多准则决策（MCDM）是运筹学的一个分支，用于在冲突标准下对备选方案进行选择、排序或分类。近年来，其应用扩展至动态、数据驱动领域，如推荐系统。在此类场景中，个人敏感数据的可用性与处理方式对决策至关重要。尽管对敏感数据依赖日益增加，但将隐私机制融入MCDM方法的研究仍不充分。本文提出一种整合方法，将MCDM的出优法与差分隐私（DP）结合，在排序问题中保护个体贡献的隐私。该方法通过预处理步骤，将多个用户评价聚合为综合性能矩阵。实验表明，真实排名与匿名化排名之间存在强至非常强的统计相关性，确保了强有力的隐私参数保障。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。