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» Optimal randomization for privacy preserving data mining
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ICDM
2009
IEEE
133views Data Mining» more  ICDM 2009»
14 years 2 months ago
On K-Means Cluster Preservation Using Quantization Schemes
This work examines under what conditions compression methodologies can retain the outcome of clustering operations. We focus on the popular k-Means clustering algorithm and we dem...
Deepak S. Turaga, Michail Vlachos, Olivier Versche...
ADC
2009
Springer
175views Database» more  ADC 2009»
14 years 2 months ago
Conditional Purpose Based Access Control Model for Privacy Protection
This paper presents a model for privacy preserving access control which is based on variety of purposes. Conditional purpose is applied along with allowed purpose and prohibited p...
Md. Enamul Kabir, Hua Wang
ICDM
2010
IEEE
150views Data Mining» more  ICDM 2010»
13 years 5 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...
KDD
2003
ACM
124views Data Mining» more  KDD 2003»
14 years 8 months ago
Information-theoretic co-clustering
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingenc...
Inderjit S. Dhillon, Subramanyam Mallela, Dharmend...
ICDM
2006
IEEE
93views Data Mining» more  ICDM 2006»
14 years 1 months ago
Comparisons of K-Anonymization and Randomization Schemes under Linking Attacks
Recently K-anonymity has gained popularity as a privacy quantification against linking attacks, in which attackers try to identify a record with values of some identifying attrib...
Zhouxuan Teng, Wenliang Du