Efficient secure primitive for privacy preserving distributed computations

Youwen Zhu, Tsuyoshi Takagi, Liusheng Huang

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

4 Citations (Scopus)

Abstract

Scalar product protocol aims at securely computing the dot product of two private vectors. As a basic tool, the protocol has been widely used in privacy preserving distributed collaborative computations. In this paper, at the expense of disclosing partial sum of some private data, we propose a linearly efficient Even-Dimension Scalar Product Protocol (EDSPP) without employing expensive homomorphic crypto-system and third party. The correctness and security of EDSPP are confirmed by theoretical analysis. In comparison with six most frequently-used schemes of scalar product protocol (to the best of our knowledge), the new scheme is a much more efficient one, and it has well fairness. Simulated experiment results intuitively indicate the good performance of our novel scheme. Consequently, in the situations where divulging very limited information about private data is acceptable, EDSPP is an extremely competitive candidate secure primitive to achieve practical schemes of privacy preserving distributed cooperative computations. We also present a simple application case of EDSPP.

Original languageEnglish
Title of host publicationAdvances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings
Pages233-243
Number of pages11
DOIs
Publication statusPublished - Nov 9 2012
Event7th International Workshop on Security, IWSEC 2012 - Fukuoka, Japan
Duration: Nov 7 2012Nov 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7631 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Security, IWSEC 2012
CountryJapan
CityFukuoka
Period11/7/1211/9/12

Fingerprint

Distributed Computation
Privacy Preserving
Scalar, inner or dot product
Experiments
Homomorphic
Partial Sums
Fairness
Correctness
Theoretical Analysis
Linearly
Computing
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhu, Y., Takagi, T., & Huang, L. (2012). Efficient secure primitive for privacy preserving distributed computations. In Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings (pp. 233-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7631 LNCS). https://doi.org/10.1007/978-3-642-34117-5-15

Efficient secure primitive for privacy preserving distributed computations. / Zhu, Youwen; Takagi, Tsuyoshi; Huang, Liusheng.

Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings. 2012. p. 233-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7631 LNCS).

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

Zhu, Y, Takagi, T & Huang, L 2012, Efficient secure primitive for privacy preserving distributed computations. in Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7631 LNCS, pp. 233-243, 7th International Workshop on Security, IWSEC 2012, Fukuoka, Japan, 11/7/12. https://doi.org/10.1007/978-3-642-34117-5-15
Zhu Y, Takagi T, Huang L. Efficient secure primitive for privacy preserving distributed computations. In Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings. 2012. p. 233-243. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-34117-5-15
Zhu, Youwen ; Takagi, Tsuyoshi ; Huang, Liusheng. / Efficient secure primitive for privacy preserving distributed computations. Advances in Information and Computer Security - 7th International Workshop on Security, IWSEC 2012, Proceedings. 2012. pp. 233-243 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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