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KDD
2003
ACM

Privacy-preserving k-means clustering over vertically partitioned data

4 years 10 months ago
Privacy-preserving k-means clustering over vertically partitioned data
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for k-means clustering when different sites contain different attributes for a common set of entities. Each site learns the cluster of each entity, but learns nothing about the attributes at other sites. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Data mining; H.2.7 [Database Management]: Database Administration--Security, integrity, and protection; H.2.4 [Database Management]: Systems--Distributed databases General Terms Security Keywords Privacy
Jaideep Vaidya, Chris Clifton
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2003
Where KDD
Authors Jaideep Vaidya, Chris Clifton
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