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» On the Design and Quantification of Privacy Preserving Data ...
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KDD
2009
ACM
142views Data Mining» more  KDD 2009»
14 years 7 months ago
Quantification and semi-supervised classification methods for handling changes in class distribution
In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier. Further complicating this scenari...
Jack Chongjie Xue, Gary M. Weiss
USS
2010
13 years 4 months ago
P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy ...
Yitao Duan, NetEase Youdao, John Canny, Justin Z. ...
ICDE
2007
IEEE
123views Database» more  ICDE 2007»
14 years 1 months ago
Privacy Preserving Pattern Discovery in Distributed Time Series
The search for unknown frequent pattern is one of the core activities in many time series data mining processes. In this paper we present an extension of the pattern discovery pro...
Josenildo Costa da Silva, Matthias Klusch
SIGMOD
2010
ACM
274views Database» more  SIGMOD 2010»
13 years 12 months ago
K-isomorphism: privacy preserving network publication against structural attacks
Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due ...
James Cheng, Ada Wai-Chee Fu, Jia Liu
FSKD
2008
Springer
136views Fuzzy Logic» more  FSKD 2008»
13 years 8 months ago
k-Anonymity via Clustering Domain Knowledge for Privacy Preservation
Preservation of privacy in micro-data release is a challenging task in data mining. The k-anonymity method has attracted much attention of researchers. Quasiidentifier is a key co...
Taiyong Li, Changjie Tang, Jiang Wu, Qian Luo, She...