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ISI
2008
Springer
13 years 5 months ago
A framework for privacy-preserving cluster analysis
Abstract--Releasing person-specific data could potentially reveal sensitive information of individuals. k-anonymization is a promising privacy protection mechanism in data publishi...
Benjamin C. M. Fung, Ke Wang, Lingyu Wang, Mourad ...
VLDB
2006
ACM
122views Database» more  VLDB 2006»
14 years 6 months ago
A secure distributed framework for achieving k-anonymity
k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous...
Wei Jiang, Chris Clifton
ICDE
2012
IEEE
221views Database» more  ICDE 2012»
11 years 8 months ago
DPCube: Releasing Differentially Private Data Cubes for Health Information
—We demonstrate DPCube, a component in our Health Information DE-identification (HIDE) framework, for releasing differentially private data cubes (or multi-dimensional histogram...
Yonghui Xiao, James J. Gardner, Li Xiong
ICDM
2010
IEEE
150views Data Mining» more  ICDM 2010»
13 years 3 months ago
Probabilistic Inference Protection on Anonymized Data
Background knowledge is an important factor in privacy preserving data publishing. Probabilistic distributionbased background knowledge is a powerful kind of background knowledge w...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang, Y...
SDM
2010
SIAM
153views Data Mining» more  SDM 2010»
13 years 7 months ago
Reconstruction from Randomized Graph via Low Rank Approximation
The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreservin...
Leting Wu, Xiaowei Ying, Xintao Wu