Privacy-preserving Apriori-based association rule mining over semantically secure encrypted cloud database

Data Mining has been widely applied in many areas such as scientific analysis, healthcare, finance and marketing. Association rule mining is one of the broadly used methods in data mining applications. In recent years, with the rapid growth of cloud computing, many academic solutions to the outsourced privacy-preserving association rule mining (PPARM) problem have been proposed under various threat models. However, existing approaches cannot simultaneously satisfy the requirements of strong security and high efficiency. In this paper, we focus on effectively solving the PPARM problem over semantically secure ciphertexts. In particular, we propose a novel scheme which executes Apriori algorithm on outsourced encrypted databases. The proposed scheme achieves database security, preserves mining privacy and supports off-line clients during the mining process. To the best of our knowledge, our work is the first to design a semantically secure PPARM scheme without on-line users. Also, we provide formal security analysis and conduct extensive experiments to evaluate the efficiency of our proposed scheme. The results indicate that our scheme can be efficiently implemented on large databases.