An Algorithm for Privacy-Preserving Quantitative Association Rules Mining

When data mining occurs on distributed data, privacy of parties becomes great concerns. This paper considers the problem of mining quantitative association rules without revealing the private information of parties who compute jointly and share distributed data. The issue is an area of privacy preserving data mining (PPDM) research. Some researchers have considered the case of mining Boolean association rules; however, this method cannot be easily applied to quantitative rules mining. A new secure set union algorithm is proposed in this paper, which unifies the input sets of parties without revealing any element´s owner and has lower time cost than existing algorithms. The new algorithm takes the advantages of both in privacy-preserving Boolean association rules mining and in privacy-preserving quantitative association mining. This paper also presents an algorithm for privacy-preserving quantitative association rules mining over horizontally portioned data, based on CF tree and secure sum algorithm. Besides, the analysis of the correctness, the security and the complexity of our algorithms are provided