On efficient and robust anonymization for privacy protection on massive streaming categorical information

Zhang, Ji and Li, Hongzhou and Liu, Xuemei and Luo, Yonglong and Chen, Fulong and Wang, Hua and Chang, Liang (2017) On efficient and robust anonymization for privacy protection on massive streaming categorical information. IEEE Transactions on Dependable and Secure Computing, 14 (5). pp. 507-520. ISSN 1545-5971

Abstract

Protecting users’ privacy when transmitting a large amount of data over the Internet is becoming increasingly important
nowadays. In this paper, we focus on the streaming categorical information and propose a novel anonymization technique for providing a strong privacy protection to safeguard against privacy disclosure and information tampering. Our technique utilizes an innovative two-phase anonymization approach which is very easy to implement, highly efficient in terms of speed and communication
and is robust against possible tampering fromadversaries. Extensive experimental evaluation that is conducted demonstrates that our technique is very efficient andmore robust than the existingmethod.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 29 Feb 2016 02:54
Last Modified: 19 Dec 2017 05:00
Uncontrolled Keywords: privacy protection, anonymization, categorical information
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1109/TDSC.2015.2483503
URI: http://eprints.usq.edu.au/id/eprint/28059

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