Systematic clustering method for l-diversity model

Kabir, Md Enamul and Wang, Hua and Bertino, Elisa and Chi, Yunxiang (2010) Systematic clustering method for l-diversity model. In: ADC 2010: 21st Australasian Conference on Database Technologies, 18-22 Jan 2010, Brisbane, Australia.

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Abstract

Nowadays privacy becomes a major concern and many research efforts have been dedicated to the development of privacy protecting technology. Anonymization techniques provide an e±cient approach to protect data privacy. We recently proposed a systematic clustering1 method based on k- anonymization technique that minimizes the information loss and at the same time assures data quality. In this paper, we extended our previous work on the systematic clustering method to l-diversity model that assumes that every group of indistinguishable records contains at least l distinct sensitive attributes values. The proposed technique adopts to group similar data together with l-diverse sensitive values and then anonymizes each group individually. The structure of systematic clustering problem for l-diversity model is defined, investigated through paradigm and is implemented in two steps, namely clustering step for k- anonymization and l-diverse step. Finally, two algorithms of the proposed problem in two steps are developed and shown that the time complexity is in O(n^2/k) in the first step, where n is the total number of records containing individuals concerning their privacy and k is the anonymity parameter for k-anonymization.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher. Copyright c 2010, Australian Computer Society, Inc. This paper appeared at the Twenty-First Australasian Database Conference (ADC2010), Brisbane, Australia, January 2010. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 103, Heng Tao Shen and Athman Bouguettaya, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.
Depositing User: Mr Md Enamul Kabir
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 12 Jul 2010 05:00
Last Modified: 05 Sep 2014 05:20
Uncontrolled Keywords: privacy; k-anonymity; l-diversity; systematic clustering
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0804 Data Format > 080402 Data Encryption
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/7326

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