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：2015
卷号：3
期号：1
DOI：10.15680/ijircce.2015.0301053
出版社：S&S Publications
摘要：This paper reviews methods to be developed for anonymizing data in cloud from 2015 to 2014 .Publishing microdata such as census or patient data for extensive research and other purposes is an important problemarea being focused by government agencies and other social associations. The traditional approach identified throughliterature survey reveals that the approach of eliminating uniquely identifying fields such as social security numberfrom microdata, still results in disclosure of sensitive data, k-anonymization optimization algorithm ,seems to bepromising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, therebyleading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and backgroundknowledge attack . The notion of l-diversity proposed in the literature to address this issue also poses a number ofconstraints , as it proved to be inefficient to prevent attribute disclosure (skewness attack and similarity attack), ldiversityis difficult to achieve and may not provide sufficient privacy protection against sensitive attribute acrossequivalence class can substantially improve the privacy as against information disclosure limitation techniques such assampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficientanonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness bykernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive attribute in amicrodata and also maintaining diversity.
