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» Privacy-Preserving Data Imputation
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DAWAK
2006
Springer
13 years 11 months ago
Two New Techniques for Hiding Sensitive Itemsets and Their Empirical Evaluation
Many privacy preserving data mining algorithms attempt to selectively hide what database owners consider as sensitive. Specifically, in the association-rules domain, many of these ...
Ahmed HajYasien, Vladimir Estivill-Castro
PVLDB
2010
95views more  PVLDB 2010»
13 years 5 months ago
Small Domain Randomization: Same Privacy, More Utility
Random perturbation is a promising technique for privacy preserving data mining. It retains an original sensitive value with a certain probability and replaces it with a random va...
Rhonda Chaytor, Ke Wang
AUSDM
2006
Springer
157views Data Mining» more  AUSDM 2006»
13 years 11 months ago
Safely Delegating Data Mining Tasks
Data mining is playing an important role in decision making for business activities and governmental administration. Since many organizations or their divisions do not possess the...
Ling Qiu, Kok-Leong Ong, Siu Man Lui
ICDM
2008
IEEE
96views Data Mining» more  ICDM 2008»
14 years 1 months ago
Filling in the Blanks - Krimp Minimisation for Missing Data
Many data sets are incomplete. For correct analysis of such data, one can either use algorithms that are designed to handle missing data or use imputation. Imputation has the bene...
Jilles Vreeken, Arno Siebes
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
13 years 9 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes