Secure two and multi-party association rule mining

Abstract

Association rule mining provides useful knowledge from raw data in different applications such as health, insurance, marketing and business systems. However, many real world applications are distributed among two or more parties, each of which wants to keep its sensitive information private, while they collaboratively gaining some knowledge from their data. Therefore, secure and distributed solutions are needed that do not have a central or third party accessing the parties' original data. In this paper, we present a new protocol for privacy-preserving association rule mining to overcome the security flaws in existing solutions, with better performance, when data is vertically partitioned among two or more parties. Two sub-protocols for secure binary dot product and cardinality of set intersection for binary vectors are also designed which are used in the main protocols as building blocks.