Satisfying privacy requirements before data anonymization
Article
Article Title | Satisfying privacy requirements before data anonymization |
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ERA Journal ID | 17794 |
Article Category | Article |
Authors | Sun, Xiaoxun (Author), Wang, Hua (Author), Li, Jiuyong (Author) and Zhang, Yanchun (Author) |
Journal Title | The Computer Journal |
Journal Citation | 55 (4), pp. 422-437 |
Number of Pages | 16 |
Year | 2012 |
Place of Publication | United Kingdom |
ISSN | 0010-4620 |
1460-2067 | |
Digital Object Identifier (DOI) | https://doi.org/10.1093/comjnl/bxr028 |
Web Address (URL) | http://comjnl.oxfordjournals.org/content/55/4/422 |
Abstract | In this paper, we study a problem of protecting privacy of individuals in large public survey rating data. We propose a novel (k,ϵ, l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be similar to at least k−1 other records based on the non-sensitive ratings, where the similarity is controlled by ϵ, and the standard deviation of sensitive ratings is at least l. We study an interesting yet non-trivial satisfaction problem of the proposed model, which is to decide whether a survey rating data set satisfies the privacy requirements given by the user. For this problem, we investigate its inherent properties theoretically, and devise a novel slicing technique to solve it. We analyze the computation complexity of the proposed slicing technique and conduct extensive experiments on two real-life data sets, and the results show that the slicing technique is fast and scalable with data size and much more efficient in terms of execution time and space overhead than the heuristic pairwise method. |
Keywords | privacy; system security; data anonymization |
ANZSRC Field of Research 2020 | 490302. Numerical analysis |
460499. Cybersecurity and privacy not elsewhere classified | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Australian Council for Educational Research, Australia |
Department of Mathematics and Computing | |
University of South Australia | |
Victoria University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q1824/satisfying-privacy-requirements-before-data-anonymization
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