MuCo：通过相互覆盖实现隐私保护的微数据发布
MuCo: Publishing Microdata with Privacy Preservation through Mutual Cover
AI总结：
本文提出相互覆盖（MuCo）技术，通过随机替换准标识符值使相似元组相互覆盖，在防止身份和属性泄露的同时比泛化方法更有效地保留微数据的信息效用。
摘要：
我们研究用于微数据发布中隐私保护的 k-匿名族匿名化技术。尽管现有基于泛化的方法能够提供足够好的保护，但泛化后的表总是遭受相当大的信息损失，主要原因是 QI（准标识符）值的分布几乎未被保留，并且查询语句的结果是分组而非具体元组。为此，我们提出一种新技术，称为相互覆盖（MuCo），以防止敌手在已发布的微数据中匹配 QI 值的组合。其原理是根据随机输出表用随机值替换一些原始 QI 值，使相似元组以最小代价相互覆盖。因此，MuCo 能够防止身份泄露和属性泄露，同时比泛化更有效地保留信息效用。MuCo 的有效性通过大量实验得到验证。
英文摘要：
We study the anonymization technique of k-anonymity family for preserving privacy in the publication of microdata. Although existing approaches based on generalization can provide good enough protections, the generalized table always suffers from considerable information loss, mainly because the distributions of QI (Quasi-Identifier) values are barely preserved and the results of query statements are groups rather than specific tuples. To this end, we propose a novel technique, called the Mutual Cover (MuCo), to prevent the adversary from matching the combination of QI values in published microdata. The rationale is to replace some original QI values with random values according to random output tables, making similar tuples to cover for each other with the minimum cost. As a result, MuCo can prevent both identity disclosure and attribute disclosure while retaining the information utility more effectively than generalization. The effectiveness of MuCo is verified with extensive experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
