DocumentCode :
2294382
Title :
Extended K-Anonymity Models Against Attribute Disclosure
Author :
Sun, Xiaoxun ; Wang, Hua ; Sun, Lili
Author_Institution :
Dept. of Math. & Comput., Univ. of Southern Queensland, Toowoomba, QLD, Australia
fYear :
2009
fDate :
19-21 Oct. 2009
Firstpage :
130
Lastpage :
136
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 :
security of data; attribute disclosure; extended k-anonymity models; p-sensitive k-anonymity model; sensitive information; specific sensitive attributes; top-down approach; Computer networks; Data security; Information security; Joining processes; Mathematical model; Mathematics; Medical conditions; Protection; Publishing; Sun;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network and System Security, 2009. NSS '09. Third International Conference on
Conference_Location :
Gold Coast, QLD
Print_ISBN :
978-1-4244-5087-9
Electronic_ISBN :
978-0-7695-3838-9
Type :
conf
DOI :
10.1109/NSS.2009.23
Filename :
5318942
Link To Document :
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