Learning Method of Fuzzy Inference Systems for Secure Multiparty Computation

  • Hirofumi Miyajima
  • Noritaka Shigei
  • Hiromi Miyajima
  • Yohtaro Miyanishi
  • Shinji Kitagami
  • Norio Shiratori
Conference paper


Many studies on privacy preserving of machine learning and data mining for cloud computing have been done in various methods by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Data encryption is one of typical approaches. However, its system requires both encryption and decryption for requests of client or user, so its complexity of computation is very high. Therefore, studies on secure computation using shared or divided data are made to avoid secure risks being abused or leaked and to reduce computing cost. The secure multiparty computation (SMC) is one of these methods. So far, some studies have been done with SMC using divided data, but complex calculation processing such as machine learning has never proposed yet. In the previous paper, we proposed BP learning method for SMC on cloud computing system. In this paper, we propose learning method (Fuzzy modeling) of fuzzy inference system for SMC and prove the validity of it. Further, the performance of the proposed method is shown in numerical simulations.


Cloud computing Control problem Data division Fuzzy modeling Privacy preserving learning method Secure multiparty computation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Hirofumi Miyajima
    • 1
  • Noritaka Shigei
    • 2
  • Hiromi Miyajima
    • 2
  • Yohtaro Miyanishi
    • 3
  • Shinji Kitagami
    • 4
  • Norio Shiratori
    • 5
  1. 1.Okayama University of ScienceOkayamaJapan
  2. 2.Kagoshima UniversityKagoshimaJapan
  3. 3.Information Systems Engineering and ManagementTokyoJapan
  4. 4.Waseda University Graduate School of Global Information and Telecommunication Studies (GITS)TokyoJapan
  5. 5.Chuo UniversityTokyoJapan

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