Secure statistical analysis using RLWE-based homomorphic encryption

Masaya Yasuda, Takeshi Shimoyama, Jun Kogure, Kazuhiro Yokoyama, Takeshi Koshiba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Homomorphic encryption enables various calculations while preserving the data confidentiality. Here we apply the homomorphic encryption scheme proposed by Brakerski and Vaikuntanathan (CRYPTO 2011) to secure statistical analysis between two variables. For reduction of ciphertext size and practical performance, we propose a method to pack multiple integers into a few ciphertexts so that it enables efficient computation over the packed ciphertexts. Our packing method is based on Yasuda et al.’s one (DPM 2013). While their method gives efficient secure computation only for small integers, our modification is effective for larger integers. Our implementation shows that our method is faster than the state-of-the-art work. Specifically, for one million integers of 16 bits (resp. 128 bits), it takes about 20 minutes (resp. 3.6 hours) for secure covariance and correlation on an Intel Core i7-3770 3.40 GHz CPU.

Original languageEnglish
Title of host publicationInformation Security and Privacy - 20th Australasian Conference, ACISP 2015, Proceedings
PublisherSpringer Verlag
Pages471-487
Number of pages17
Volume9144
ISBN (Print)9783319199610
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event20th Australasian Conference on Information Security and Privacy, ACISP 2015 - Brisbane, Australia
Duration: 2015 Jun 292015 Jul 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9144
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th Australasian Conference on Information Security and Privacy, ACISP 2015
CountryAustralia
CityBrisbane
Period15/6/2915/7/1

Keywords

  • Homomorphic encryption
  • Packing methods
  • Ring-LWE assumption
  • Secure covariance and correlation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yasuda, M., Shimoyama, T., Kogure, J., Yokoyama, K., & Koshiba, T. (2015). Secure statistical analysis using RLWE-based homomorphic encryption. In Information Security and Privacy - 20th Australasian Conference, ACISP 2015, Proceedings (Vol. 9144, pp. 471-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9144). Springer Verlag. https://doi.org/10.1007/978-3-319-19962-7_27