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2018 Fiscal Year Annual Research Report

秘密計算において,ゲノムデータを用いた複数な検定のアウトソーシング計算基盤

Research Project

Project/Area Number 17J00450
Research InstitutionUniversity of Tsukuba

Principal Investigator

陸 文杰  筑波大学, システム情報工学研究科, 特別研究員(DC2)

Project Period (FY) 2017-04-26 – 2019-03-31
Keywords行列積の秘密計算 / 非対話型な大小比較秘密計算
Outline of Annual Research Achievements

We present a more practical and non-interactive comparison protocol for ciphertexts of small domain integers, e.g., range in [0, 2^13). Our comparison protocol uses only one homomorphic multiplication to compare two ciphertext, which is about 45xー75x times faster than the existing homomorphic encryption-based solutions.

The second paper focuses on the matrix multiplication on encrypted matrices. We present a band new packing, double packing, for batching the inner products of K encrypted vectors, where K is about 100. On top of the double packing, we present a communication efficient and computation fast matrix multiplication protocol, which is about 10x faster than the existing HE-based methods and consumes only 1/40 commutation bandwidth of its OT-based counterparts.

Research Progress Status

平成30年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

平成30年度が最終年度であるため、記入しない。

  • Research Products

    (4 results)

All 2018 Other

All Int'l Joint Research (2 results) Presentation (2 results) (of which Int'l Joint Research: 2 results)

  • [Int'l Joint Research] MIT(米国)

    • Country Name
      U.S.A.
    • Counterpart Institution
      MIT
  • [Int'l Joint Research] Alibaba Group(中国)

    • Country Name
      CHINA
    • Counterpart Institution
      Alibaba Group
  • [Presentation] Non-interactive and Output Expressive Private Comparison from Homomorphic Encryption and the Applications2018

    • Author(s)
      Wen-jie Lu, Jun-jie Zhou, and Jun Sakuma
    • Organizer
      The13th ACM ASIA Conference on Information, Computer and Communications Security
    • Int'l Joint Research
  • [Presentation] More Practical Privacy-Preserving Machine Learning as A Service via Efficient Secure Matrix Multiplication2018

    • Author(s)
      Wen-jie Lu, and Jun Sakuma
    • Organizer
      The 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography
    • Int'l Joint Research

URL: 

Published: 2019-12-27  

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