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» On the Design and Quantification of Privacy Preserving Data ...
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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
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 7 months ago
Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework
Abstract. As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed mode...
Rhonda Chaytor
KDD
2010
ACM
232views Data Mining» more  KDD 2010»
13 years 11 months ago
Discovering frequent patterns in sensitive data
Discovering frequent patterns from data is a popular exploratory technique in data mining. However, if the data are sensitive (e.g. patient health records, user behavior records) ...
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, Abhr...
SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
14 years 4 months ago
Speeding Up Secure Computations via Embedded Caching.
Most existing work on Privacy-Preserving Data Mining (PPDM) focus on enabling conventional data mining algorithms with the ability to run in a secure manner in a multi-party setti...
K. Zhai, W. K. Ng, A. R. Herianto, S. Han
KDD
2009
ACM
133views Data Mining» more  KDD 2009»
14 years 7 months ago
On the tradeoff between privacy and utility in data publishing
In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility o...
Tiancheng Li, Ninghui Li