µ-Fractal based data perturbation algorithm for privacy protection

Zhong, J, Mirchandani, V, Bertok, P and Harland, J 2012, 'µ-Fractal based data perturbation algorithm for privacy protection', in Shan L. Pan and Tru H. Cao (ed.) Proceedings of the 16th Pacific Asia Conference on Information Systems (PACIS) 2012, Ho Chi Minh City, Vietnam, 11-15 July, 2012, pp. 1-15.

Document type: Conference Paper
Collection: Conference Papers

Title µ-Fractal based data perturbation algorithm for privacy protection
Author(s) Zhong, J
Mirchandani, V
Bertok, P
Harland, J
Year 2012
Conference name PACIS 2012
Conference location Ho Chi Minh City, Vietnam
Conference dates 11-15 July, 2012
Proceedings title Proceedings of the 16th Pacific Asia Conference on Information Systems (PACIS) 2012
Editor(s) Shan L. Pan and Tru H. Cao
Publisher Association for Information Systems (AIS)
Place of publication United States
Start page 1
End page 15
Total pages 15
Abstract Many organizations publish anonymous medical data for sociology research, health research, education and other useful studies. Although attributes that clearly identify individuals, such as name and certain personal identity numbers are removed, the combination of some other information, like the date of birth, gender, post-code etc. can still be used to identify an individual. Existing data perturbation techniques are able to de-identify the data prior to publishing, but they suffer from making the process irreversible, so that the original data cannot be fully recovered. How to maintain the usability and utility of privacy-protected data as well as make the published data restorable for authorized users is a major issue. In this paper, we propose a novel robust data perturbation algorithm that can withstand brute force attacks, while the perturbed data pattern is indistinguishable from the original data pattern. A distinguishing feature of our data perturbation method is that, using fractal theory to derive perturbation vectors, it provides high privacy protection together with fully reversible data perturbation while maintaining maximal data utility. Experiments based on practical data confirm the desired operation of our data perturbation algorithm and its effectiveness. The results obtained from our experiments leads us to conclude that the proposed approach is able to computationally resist brute-force attacks as well as maintain the same data distribution type as that of original data.
Subjects Data Format not elsewhere classified
Data Encryption
Computer System Security
Keyword(s) algorithm
data: privacy
Copyright notice © 2012 Association for Information Systems (AIS)
Version Filter Type
Access Statistics: 199 Abstract Views  -  Detailed Statistics
Created: Tue, 28 May 2013, 13:54:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us