Article: Incremental processing and indexing for (k, e)-anonymisation Journal: International Journal of Information and Computer Security (IJICS) 2013 Vol.5 No.3 pp.151 - 170 Abstract: The emerging of the internet-based services poses a privacy threat to the individuals. Data transformation to meet a privacy standard becomes a requirement for typical data processing for the services. (k, e)-anonymisation is one of the most promising data transformation approaches, since it can provide high-accuracy aggregate query results. Though, the computational cost of the algorithm providing optimal solutions for such approach is not very high, i.e., O(n²). In certain environments, the data to be processed can be appended at any time. In this paper, we address an efficiency issue of the incremental privacy preservation using (k, e)-anonymisation approach. The impact of the increment is observed theoretically. We propose an incremental algorithm based on such observation. The algorithm can replace the quadratic-complexity processing by a linear function on some part of the dataset, while the optimal results are guaranteed. Additionally, a few indexes are proposed to further improve the efficiency of the proposed algorithm. The experiments have been conducted to validate our work. From the results, it can be seen that the proposed work is highly efficient comparing with the non-incremental algorithm and an approximation algorithm. Inderscience Publishers - linking academia, business and industry through research

Title: Incremental processing and indexing for (k, e)-anonymisation

Authors: Juggapong Natwichai; Xue Li; Asanee Kawtrkul

Addresses: Computer Engineering Department, Faculty of Engineering, Chiang Mai University, 239 Huay Kaew Road, Suthep, Chiang Mai 50200, Thailand ' School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia ' Computer Engineering Department, Faculty of Engineering, Kasetsart University, Ladyao Chatuchak, Bangkok 10900, Thailand

Abstract: The emerging of the internet-based services poses a privacy threat to the individuals. Data transformation to meet a privacy standard becomes a requirement for typical data processing for the services. (k, e)-anonymisation is one of the most promising data transformation approaches, since it can provide high-accuracy aggregate query results. Though, the computational cost of the algorithm providing optimal solutions for such approach is not very high, i.e., O(n²). In certain environments, the data to be processed can be appended at any time. In this paper, we address an efficiency issue of the incremental privacy preservation using (k, e)-anonymisation approach. The impact of the increment is observed theoretically. We propose an incremental algorithm based on such observation. The algorithm can replace the quadratic-complexity processing by a linear function on some part of the dataset, while the optimal results are guaranteed. Additionally, a few indexes are proposed to further improve the efficiency of the proposed algorithm. The experiments have been conducted to validate our work. From the results, it can be seen that the proposed work is highly efficient comparing with the non-incremental algorithm and an approximation algorithm.

Keywords: incremental processing; privacy preservation; anonymisation; indexing; security.

DOI: 10.1504/IJICS.2013.055836

International Journal of Information and Computer Security, 2013 Vol.5 No.3, pp.151 - 170

Published online: 30 Jul 2014 *

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Title: Incremental processing and indexing for (k, e)-anonymisation

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