Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing
Yang Xiao · Chinese Journal of Computers · 2008
Current privacy preserving data publishing techniques concentrate on tables with only one sensitive attribute. However, most of the real-world applications contain multiple sensitive attributes. Directly applying the existing single-sensitive-attribute privacy preserving techniques often causes unexpected private information disclosure. This paper firstly discusses the problem of secure publishing data when sensitive data contains multi attributes, and then propose a multi-dimensional bucket grouping approach on the idea of lossy join, called Multi-Sensitive Bucketization (MSB). In order to avoid exhausting search, three specific line-time greedy based MSB algorithms are proposed, which are maximal-bucket first algorithm (MBF), maximal single-dimension-capacity first algorithm (MSDCF), and maximal multi-dimension-capacity first algorithm (MMDCF). In addition, according to the differences among published data, a weighted MSB approach is further proposed. Experimental results on the real-world datasets show that the addition information loss of the proposed MSB methods were not more than 0.04 and the suppression ratios were less than 0.06. The weighted MSB approach can guarantee more than 70% publishing ratio.