文章基本信息

标题：SW-SDF Based Personal Privacy with QIDB-Anonymization Method
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作者：Kiran P ; Dr Kavya N P
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2012
卷号：3
期号：8
DOI：10.14569/IJACSA.2012.030811
出版社：Science and Information Society (SAI)
摘要：Personalized anonymization is a method in which a guarding node is used to indicate whether the record owner is ready to reveal its sensitivity based on which anonymization will be performed. Most of the sensitive values that are present in the private data base do not require privacy preservation since the record owner sensitivity is a general one. So there are only few records in the entire distribution that require privacy. For example a record owner having disease flu doesn’t mind revealing his identity as compared to record owner having disease cancer. Even in this some of the record owners who have cancer are ready to reveal their identity, this is the motivation for SW-SDF based Personal Privacy. In this paper we propose a novel personalized privacy preserving technique that over comes the disadvantages of previous personalized privacy and other anonymization techniques. The core of this method can be divided in to two major components. The first component deals with additional attribute used in the table which is in the form of flags which can be used to divide sensitive attribute. Sensitive Disclosure Flag (SDF) determines whether record owner sensitive information is to be disclosed or whether privacy should be maintained. The second flag that we are using is Sensitive Weigh (SW) which indicates how much sensitive the attribute value is as compared with the rest. Second section deals with a novel representation called Frequency Distribution Block (FDB) and Quasi–Identifier Distribution Block(QIDB) which is used in anonymization. Experimental result show that it has lesser information loss and faster execution time as compared with existing methods.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Privacy Peserving Data Mining(PPDM);Privacy Preserving Data Publishing(PPDP); Personal Anonymization.