期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2013
卷号：3
期号：3
页码：6-8
出版社：International Journal of Soft Computing & Engineering
摘要：In recent years privacy preservation micro data publishing has gained wide popularity. Two of the most widely used anonymization techniques are generalization and bucketization. Bucketization doesn’t prevent membership disclosure and it doesn’t apply for data that don’t have a clear distinction between quasi-identifiers and sensitive attribute. On the other hand, generalization loses high amount of data. A combination of both i.e., slicing provides better data utility but still its prone to attacks. Slicing protects the data against membership and attribute disclosure but it doesn’t provide any details about identity disclosure. To overcome this we apply k-anonymity through ranging which will improve the overall utility and privacy of data. Here the data is not lost as well as it doesn’t result in inference attacks
关键词：Anonymization; Data Privacy; Privacy;Preservation; Slicing
