Privacy-preserving distributed association rule mining based on the secret sharing technique

Due to privacy law and motivation of business interests, privacy is concerned and has become an important issue in data mining. This paper explores the issue of privacy-preserving distributed association rule mining in vertically partitioned data among multiple parties, and proposes a collusion-resistant algorithm of distributed association rule mining based on the Shamir´s secret sharing technique, which prevents effectively the collusive behaviors and conducts the computations across the parties without compromising their data privacy. Additionally, analyses with regard to the security, efficiency and correctness of the proposed algorithm are given.