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» Privacy-Preserving Data Imputation
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VLDB
2007
ACM
137views Database» more  VLDB 2007»
14 years 7 months ago
Fast Data Anonymization with Low Information Loss
Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy preserving paradigms of k-anonymity and -diversity. k-a...
Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, ...
CASCON
2004
129views Education» more  CASCON 2004»
13 years 9 months ago
Building predictors from vertically distributed data
Due in part to the large volume of data available today, but more importantly to privacy concerns, data are often distributed across institutional, geographical and organizational...
Sabine M. McConnell, David B. Skillicorn
ADC
2007
Springer
145views Database» more  ADC 2007»
14 years 1 months ago
The Privacy of k-NN Retrieval for Horizontal Partitioned Data -- New Methods and Applications
Recently, privacy issues have become important in clustering analysis, especially when data is horizontally partitioned over several parties. Associative queries are the core retr...
Artak Amirbekyan, Vladimir Estivill-Castro
BMCBI
2008
190views more  BMCBI 2008»
13 years 7 months ago
Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many meth...
Guy N. Brock, John R. Shaffer, Richard E. Blakesle...
ACSW
2007
13 years 9 months ago
Controlling Inference: Avoiding P-level Reduction during Analysis
This paper presents a concept hierarchy-based approach to privacy preserving data collection for data mining called the P-level model. The P-level model allows data providers to d...
Adepele Williams, Ken Barker