文章基本信息

标题：Term Frequency Based Sequence Generation Algorithm for Graph Based Data Anonymization
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作者：S.Charanyaa ; K.Sangeetha
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：2
出版社：S&S Publications
摘要：A big deal of research has been performed in the area of graphical data anonymization. Because of the wide range of application of graphical data from social network data to large warehouse data and knowledge engineering domains. Notion of k - anonymity has been proposed in literature, which is a framework for protecting privacy, emphasizing the lemma that a database to be k - anonymous, every tuple should be different fro m at least k - 1 other tuples in accordance with their quasi - identifiers(QIDs). Inspite of the existence of k - anonymity framework, malicious users and misfeasers may get authorization to the sensitive information if a set of nodes exhibit alike attributes. I n this paper we make a systematic analysis on structure anonymization mechanisms and models projected in the literature. Also we discuss the simulation analysis of KDLD model creation and construction. We propose a Term Frequency Based Sequence Generation Algorithm (TFSGA) which creates node sequence based on term frequency of tuples with minimal distortion. We experime n tally show the efficiency of the proposed algorithm under varying cluster sizes.
关键词：Data Anonymization ; Graphical Data; Sensitive ; information; kanonymity; l ; diversity; Database ; Privacy; Cluster