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Distributed Secure Data Mining with Updating Database Using Fully Homomorphic Encryption

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Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019 (IMCOM 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 935))

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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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References

  1. Zhao, Q., Bhowmick, S.S.: Association rule mining: a survey. Technical report, CAIS, Nanyang Technological University, Singapore, (2003116):1 (2003)

    Google Scholar 

  2. Chen, M.-S., Han, J., Philip, S.Y.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)

    Article  Google Scholar 

  3. Imabayashi, H., Ishimaki, Y., Umayabara, A., Sato, H., Yamana, H.: Secure frequent pattern mining by fully homomorphic encryption with ciphertext packing. In: International Workshop on Data Privacy Management, pp. 181–195. Springer (2016)

    Google Scholar 

  4. Imabayashi, H., Ishimaki, Y., Umayabara, A., Yamana, H.: Fast and space-efficient secure frequent pattern mining by FHE. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3983–3985, December 2016

    Google Scholar 

  5. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  6. Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the Twelfth International Conference on Data Engineering, pp. 106–114, February 1996

    Google Scholar 

  7. Gentry, C., et al.: Fully homomorphic encryption using ideal lattices. In: STOC, vol. 9, pp. 169–178 (2009)

    Google Scholar 

  8. Rakesh, A., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)

    Google Scholar 

  9. Cheung, D.W.-L., Ng, V.T.Y., Tam, B.W.: Maintenance of discovered knowledge: a case in multi-level association rules. In: KDD, vol. 96, pp. 307–310 (1996)

    Google Scholar 

  10. Liu, J., Li, J., Xu, S., Fung, B.C.M.: Secure outsourced frequent pattern mining by fully homomorphic encryption. In: International Conference on Big Data Analytics and Knowledge Discovery, pp. 70–81. Springer (2015)

    Google Scholar 

  11. Shoup, V., Halevi, S.: HElib. http://shaih.github.io/HElib/index.html. Accessed Jan 2017

  12. Open MPI. https://www.open-mpi.org/. Accessed Jan 2017

  13. Pacheco, P.S.: Parallel Programming with MPI. Morgan Kaufmann, San Francisco (1997)

    MATH  Google Scholar 

  14. Boost. http://www.boost.org/. Accessed Jan 2017

  15. Breshears, C.: The Art of Concurrency: A Thread Monkey’s Guide to Writing Parallel Applications. O’Reilly Media Inc., Sebastopol (2009)

    Google Scholar 

  16. Halevi, S., Shoup, V.: Algorithms in HElib. Cryptology ePrint Archive, Report 2014/106 (2014). http://eprint.iacr.org/2014/106

    Chapter  Google Scholar 

  17. Ishimaki, Y., Imabayashi, H., Yamana, H.: Private substring search on homomorphically encrypted data. In: IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6. IEEE (2017)

    Google Scholar 

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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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Authors and Affiliations

  1. Ochanomizu University, 2-1-1 Otsuka, Tokyo, 1120012, Japan

    Yuri Yamamoto & Masato Oguchi

Authors
  1. Yuri Yamamoto
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  2. Masato Oguchi
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Corresponding author

Correspondence to Yuri Yamamoto .

Editor information

Editors and Affiliations

  1. Intelligent Systems Research Institute, Sungkyunkwan University, Suwon, Korea (Republic of)

    Sukhan Lee

  2. Software Engineering, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

    Roslan Ismail

  3. College of Software, Sungkyunkwan University, Suwon, Korea (Republic of)

    Hyunseung Choo

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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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