期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：3
出版社：S&S Publications
摘要：Privacy becoming a major concern in publishing sensitive information of the ind ividuals in the soci al network data. A lot of anonymization techniques are evolved to protect sensitive information of individuals. k - anonymity is one of the data anonymization framework for protecting privacy that emphasizes the lemma, every tuple should be different from at least k - 1 other tuples in accordance with their quasi - identifiers(QIDs). Researchers have developed privacy models si milar to k - anonymity but still label - node relationship is not well protected. In this paper, we propose a novel synerg ized k - degree l - diversity t - closeness model to effectively anonymize graphical data at marginal information loss, thereby controlling the distribution of sensitive information in graphical structure based on the threshold value. Experimental evidences indi cate the substantial reduction in information loss ratio during synergy.
关键词：Data Anonymization; Graphical Data; Sensitive information; k ; anonymit y; dive ; rsity; t ; closeness
