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

19 Citations (Scopus)


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
EditorsErnest Foo, Douglas Stebila
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319199610
Publication statusPublished - 2015
Event20th Australasian Conference on Information Security and Privacy, ACISP 2015 - Brisbane, Australia
Duration: Jun 29 2015Jul 1 2015

Publication series

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


Other20th Australasian Conference on Information Security and Privacy, ACISP 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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