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