k-anonymity is a popular measure of privacy for data publishing: It measures the risk of identity-disclosure of individuals whose personal information are released in the form of ...
Bijit Hore, Ravi Chandra Jammalamadaka, Sharad Meh...
Data publishing generates much concern over the protection of individual privacy. In the well-known kanonymity model and the related models such as l-diversity and (α, k)-anonymi...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang, J...
Most previous research on privacy-preserving data publishing, based on the k-anonymity model, has followed the simplistic approach of homogeneously giving the same generalized val...
Wai Kit Wong, Nikos Mamoulis, David Wai-Lok Cheung
In some applications of privacy preserving data publishing, a practical demand is to publish a data set on multiple quasi-identifiers for multiple users simultaneously, which poses...
Motivated by the insufficiency of the existing quasi-identifier/sensitiveattribute (QI-SA) framework on modeling real-world privacy requirements for data publishing, we propose ...
Xin Jin, Mingyang Zhang, Nan Zhang 0004, Gautam Da...