caa a22 4500 445869577 CHVBK 20180317145507.0 cr unu---uuuuu 170323e20110201xx s 000 0 eng 10.1007/s00236-010-0131-6 doi (NATIONALLICENCE)springer-10.1007/s00236-010-0131-6 Efficient systematic clustering method for k -anonymization [Elektronische Daten] [Md. Kabir, Hua Wang, Elisa Bertino] This paper presents a clustering (Clustering partitions record into clusters such that records within a cluster are similar to each other, while records in different clusters are most distinct from one another.) based k-anonymization technique to minimize the information loss while at the same time assuring data quality. Privacy preservation of individuals has drawn considerable interests in data mining research. The k-anonymity model proposed by Samarati and Sweeney is a practical approach for data privacy preservation and has been studied extensively for the last few years. Anonymization methods via generalization or suppression are able to protect private information, but lose valued information. The challenge is how to minimize the information loss during the anonymization process. We refer to the challenge as a systematic clustering problem for k-anonymization which is analysed in this paper. The proposed technique adopts group-similar data together and then anonymizes each group individually. The structure of systematic clustering problem is defined and investigated through paradigm and properties. An algorithm of the proposed problem is developed and shown that the time complexity is in $${O(\frac{n^{2}}{k})}$$, where n is the total number of records containing individuals concerning their privacy. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time. Finally the algorithm illustrates the usability for incremental datasets. Springer-Verlag, 2011 Kabir Md Department of Mathematics and Computing, University of Southern Queensland, 4350, Toowoomba, QLD, Australia aut Wang Hua Department of Mathematics and Computing, University of Southern Queensland, 4350, Toowoomba, QLD, Australia aut Bertino Elisa Department of Computer Science and CERIAS, Purdue University, West Lafayette, IN, USA aut Acta Informatica Springer-Verlag 48/1(2011-02-01), 51-66 0001-5903 48:1<51 2011 48 236 https://doi.org/10.1007/s00236-010-0131-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00236-010-0131-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kabir Md Department of Mathematics and Computing, University of Southern Queensland, 4350, Toowoomba, QLD, Australia aut NATIONALLICENCE 700 1- Wang Hua Department of Mathematics and Computing, University of Southern Queensland, 4350, Toowoomba, QLD, Australia aut NATIONALLICENCE 700 1- Bertino Elisa Department of Computer Science and CERIAS, Purdue University, West Lafayette, IN, USA aut NATIONALLICENCE 773 0- Acta Informatica Springer-Verlag 48/1(2011-02-01), 51-66 0001-5903 48:1<51 2011 48 236 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer