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
We identify proximity breach as a privacy threat specific to numerical sensitive attributes in anonymized data publication. Such breach occurs when an adversary concludes with hig...
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...
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...
Previous works about privacy preserving serial data publishing on dynamic databases have relied on unrealistic assumptions of the nature of dynamic databases. In many applications...
Yingyi Bu, Ada Wai-Chee Fu, Raymond Chi-Wing Wong,...