Abstract—Several techniques are used in data analysis, where frequent itemset mining and association rule mining are very popular among them. The motivation for ‘Data Mining as a Service’ (DMaaS) paradigm is that when the data owners are not capable of doing mining tasks internally they have to outsource the mining work to a trusted third party. Multiple data owners can also collaboratively mine by combining their databases. In such cases the privacy of outsourced data is a major issue. Here the context includes necessity of ‘corporate privacy’ which means other than the data, the result of mining should also preserve privacy requirements. The system proposed uses Advanced Encryption Standard (AES) to encrypt the data items before outsourcing in order to prevent the vulnerability of ‘Known Plaintext’ attack in the existing system. Fictitious transactions are inserted to the databases using k-anonymity method to counter the frequency analysis attack. A symmetric homomorphic encryption scheme is applied in the databases for performing the mining securely. Based on the experiments and findings, though the running time of proposed solution is slightly greater than the existing system, it provides better security to the data items. Since the computations tasks are performed by the third party server, consumption of resources at the data owners’ side is very less.
Keywords—privacy preserving, association rule mining, data mining, vertically partitioned databases, homomorphic encryption.
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