Extended k-anonymity models against sensitive attribute disclosure
Article
Article Title | Extended k-anonymity models against sensitive attribute disclosure |
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ERA Journal ID | 5063 |
Article Category | Article |
Authors | Sun, Xiaoxun (Author), Sun, Lili (Author) and Wang, Hua (Author) |
Journal Title | Computer Communications |
Journal Citation | 34 (4), pp. 526-535 |
Number of Pages | 10 |
Year | 2011 |
Place of Publication | Netherlands |
ISSN | 0140-3664 |
1873-703X | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.comcom.2010.03.020 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0140366410001295 |
Abstract | p-Sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a set of quasi-identifier attributes. In this paper, we identify the situations when the p-sensitive k-anonymity property is not enough for the sensitive attributes protection. To overcome the shortcoming of the p-sensitive k-anonymity principle, we propose two new enhanced privacy requirements, namely p+-sensitive k-anonymity and (p,α)-sensitive k-anonymity properties. These two new introduced models target at different perspectives. Instead of focusing on the specific values of sensitive attributes, p+-sensitive k-anonymity model concerns more about the categories that the values belong to. Although (p,α)-sensitive k-anonymity model still put the point on the specific values, it includes an ordinal metric system to measure how much the specific sensitive attribute values contribute to each QI-group. We make a thorough theoretical analysis of hardness in computing the data set that satisfies either p+-sensitive k-anonymity or (p,α)-sensitive k-anonymity. We devise a set of algorithms using the idea of top-down specification, which is clearly illustrated in the paper. We implement our algorithms on two real-world data sets and show in the comprehensive experimental evaluations that the two new introduced models are superior to the previous method in terms of effectiveness and efficiency. |
Keywords | k-anonymity; NP-hard; attribute disclosure; algorithm |
ANZSRC Field of Research 2020 | 461399. Theory of computation not elsewhere classified |
460499. Cybersecurity and privacy not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q077x/extended-k-anonymity-models-against-sensitive-attribute-disclosure
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