Abstract
The rapid advances in recent years in the field of data mining have lead to concern about privacy. The main aim of privacy preserving data mining is to find the global mining results without leaking individual information. For satisfying the privacy constraints, algorithms based on cryptography techniques, data perturbation, information hiding, k-anonymization, secure scalar product, and secret sharing technique are used. In this paper, we propose secure protocol for association rule mining using vector dot product over vertically distributed data among multiple parties. Our method is secure, more efficient and requires less communication cost.
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Kharat, R., Kumbhar, M., Bhamre, P. (2014). Efficient Privacy Preserving Distributed Association Rule Mining Protocol Based on Random Number. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_83
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DOI: https://doi.org/10.1007/978-81-322-1665-0_83
Publisher Name: Springer, New Delhi
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