Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A practical approach to solve Secure Multi-party Computation problems
Proceedings of the 2002 workshop on New security paradigms
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging the "Multi" in secure multi-party computation
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
| Hi-index | 0.00 |
Privacy preserving distributed data mining has become a promising research area. This paper addresses the problem of association rule mining where the global database is vertically partitioned. When transactions are distributed in different sites, scalar product is a feasible tool to discover frequent itemsets. We present a new protocol to compute scalar product between two parties with a permutation approach. We analyze the protocol in detail and demonstrate its effectiveness and high privacy properties, and compare it to other published protocols.