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

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Jaideep Vaidya, Chris Clifton
• Keywords: Data Mining, Database Applications-Data Mining, Database Management, KDD 2003, Security Keywords Privacy