Recently, privacy issues have become important in clustering analysis, especially when data is horizontally partitioned over several parties. Associative queries are the core retr...
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 ...
Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, searching and indexing of releva...
Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper we prop...
Most existing work on Privacy-Preserving Data Mining (PPDM) focus on enabling conventional data mining algorithms with the ability to run in a secure manner in a multi-party setti...