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» Privacy in Data Mining Using Formal Methods
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ICDE
2009
IEEE
192views Database» more  ICDE 2009»
14 years 9 months ago
Deriving Private Information from Association Rule Mining Results
Data publishing can provide enormous benefits to the society. However, due to privacy concerns, data cannot be published in their original forms. Two types of data publishing can a...
Zutao Zhu, Guan Wang, Wenliang Du
KDD
2004
ACM
137views Data Mining» more  KDD 2004»
14 years 8 months ago
When do data mining results violate privacy?
Privacy-preserving data mining has concentrated on obtaining valid results when the input data is private. An extreme example is Secure Multiparty Computation-based methods, where...
Murat Kantarcioglu, Jiashun Jin, Chris Clifton
TKDE
2012
227views Formal Methods» more  TKDE 2012»
11 years 10 months ago
Publishing Search Logs - A Comparative Study of Privacy Guarantees
—Search engine companies collect the “database of intentions”, the histories of their users’ search queries. These search logs are a gold mine for researchers. Search engin...
Michaela Götz, Ashwin Machanavajjhala, Guozha...
ADMA
2008
Springer
186views Data Mining» more  ADMA 2008»
14 years 2 months ago
A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree
Association rules mining is a frequently used technique which finds interesting association and correlation relationships among large set of data items which occur frequently toge...
Chunhua Su, Kouichi Sakurai
ICDM
2009
IEEE
130views Data Mining» more  ICDM 2009»
13 years 5 months ago
Efficient Anonymizations with Enhanced Utility
The k-anonymization method is a commonly used privacy-preserving technique. Previous studies used various measures of utility that aim at enhancing the correlation between the orig...
Jacob Goldberger, Tamir Tassa