Abstract
Uploading commercial data to third-party cloud services is popular in general. To further promote the active utilization of big data, outsourcing data mining systems that can execute statistical calculations using the uploaded data have been proposed. In this case, personal and sensitive data are required to be encrypted for privacy protection. In previous research, data protection using fully homomorphic encryption (FHE) was proposed for a client/server secret data mining system using the Apriori algorithm. However, this system requires much time because of the computational complexity of FHE calculations. Additionally, although frequent database updates occurred in the practical use of the system, the Apriori algorithm needs recalculation of the whole database at each update. In this study, to solve these two problems, we proposed the implementation of a master/worker distributed system using the FUP algorithm, which generates candidate item sets efficiently while updating the database. We improved execution time of the secure data mining system and made it suitable for practical use.
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Acknowledgment
The author would like to thank members of the Yamana Laboratory of Waseda University and the Yamaguchi Laboratory of Kogakuin University for their valuable advice. This work was partly supported by JST CREST Grant Number JPMJCR1503, Japan.
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Yamamoto, Y., Oguchi, M. (2019). Distributed Secure Data Mining with Updating Database Using Fully Homomorphic Encryption. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_73
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