期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：1
页码：19005
DOI：10.15680/IJIRSET.2015.0401077
出版社：S&S Publications
摘要：This paper reviews methods developed for anonymizing data from 2004 to 2006 . Publishing microdatasuch as census or patient data for extensive research and other purposes is an important problem area being focused bygovernment agencies and other social associations. The traditional approach identified through literature survey revealsthat the approach of eliminating uniquely identifying fields such as social security number from microdata, still resultsin disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certaincases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computationalchallenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversityproposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficientto prevent attribute disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may notprovide sufficient privacy protection against sensitive attribute across equivalence class can substantially improve theprivacy as against information disclosure limitation techniques such as sampling cell suppression rounding and dataswapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning ofmicrodata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distancesby controlling the distribution pattern of sensitive attribute in a microdata and also maintaining diversity.
