应用模糊集保护隐私信息

Privacy preserving using fuzzy sets

数据转换是保护数据隐私的一种有效方法。针对如何保持转换后数据的可用性问题,提出了一种基于模糊集的隐私保护方法。该方法把隐私属性值转换成模糊值,然后把转换后的数据及其模糊偏移度一起公开,既保护了数据隐私,也标示了数据的相对大小,很好地保持了数据的可用性。实验采用k-平均聚类方法对转换前后的数据进行聚类分析对比,结果表明,转换前后数据的聚类结果有很高的相似性,满足保护隐私和保持可用性的要求。

Data transformation is an important approach to data privacy preserving.This paper concentrates on the issue of maintaining the usability of data after transformation and proposes a method based on fuzzy sets.The method transforms sensitive attribute values into fuzzy values and publicizes the data with fuzzy offset degree.This helps the end user to tell the different between two attribute values,even though they are mapped to the same linguistic term.The experimental analysis is designed by adopting clustering algorithm k-means on primitive datasets and perturbed ones by fuzzy sets.It demonstrates that the method efficiently preserves privacy information and maintains the clustering model of primitive data well.