基于模糊集的隐私保护方法研究  

Fuzzy-based methods for privacy preserving

作　　者：王茜[1] 杨传栋[1] 刘泓[1] 

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《计算机应用研究》2013年第2期518-520,共3页Application Research of Computers

摘　　要：在数据发布的隐私保护研究中,针对k-匿名方法的复杂性高、效率低及数据可用性差等问题,从基于模糊集的角度出发进行隐私保护的研究,重点是对数值型属性的处理,提出了基于模糊集的最大隶属度(MMD)算法。该算法对敏感数值型数据进行模糊化处理,把其变成语义型数据,结合隶属度一起发布以达到隐私保护的目的。并通过实验进行了验证,基于模糊集的隐私保护方法与k-匿名方法相比,具有更高的效率,且信息损失要远远小得多,发布数据的可用性更好。This paper did research based on fuzzy sets to overcome the high complexity, low efficiency and poor data availabil- ity of k-anonymity in the research of privacy-preserving data publishing. It focused on the processing of numerical attributes, and proposed the maximal membership degree algorithm. It fuzzed sensitive numerical attributes to semantic data which was re- leased combining with membership degree. Verified through experiments, compared with k-anonymity methods, the MMD has better efficiency, furthermore, its information losses will be far smaller than k-anonymity and the availability of released data is better.

关 键 词：隐私保护 模糊集 模糊化 隶属函数 隶属度 K-匿名 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]