Extended K-anonymity models against attribute disclosure
Paper
Paper/Presentation Title | Extended K-anonymity models against attribute disclosure |
---|---|
Presentation Type | Paper |
Authors | Sun, Xiaoxun (Author), Wang, Hua (Author) and Sun, Lili (Author) |
Journal or Proceedings Title | Proceedings of the 3rd International Conference on Network and System Security (NSS2009) |
Number of Pages | 7 |
Year | 2009 |
Place of Publication | United States |
ISBN | 9780769538389 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/NSS.2009.23 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/5318942 |
Conference/Event | NSS 2009: 3rd International Conference on Network and System Security |
Event Details | Rank B B B B B B B B B |
Event Details | NSS 2009: 3rd International Conference on Network and System Security Event Date 19 to end of 21 Oct 2009 Event Location Gold Coast, Australia |
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 combination of key attributes. However, as shown in this paper, it may not protect sensitive information in some way. In this paper, we empirically investigate two enhanced K-anonymity models. Instead of publishing original specific sensitive attributes, the new models publish the categories that the sensitive values belong to. We propose a top-down approach to implement two enhanced models and show in the comprehensive experimental evaluations that the two new introduced models are practical in terms of effectiveness and efficiency. |
Keywords | attribute disclosure; extended k-anonymity models; p-sensitive k-anonymity model; sensitive information; specific sensitive attributes; top-down approach |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
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 |
https://research.usq.edu.au/item/9z5w0/extended-k-anonymity-models-against-attribute-disclosure
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