Computational soundness of uniformity properties for multi-party computation based on LSSS

Hui Zhao, Kouichi Sakurai

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

Abstract

We provide a symbolic model for multi-party computation based on linear secret-sharing scheme, and prove that this model is computationally sound: if there is an attack in the computational world, then there is an attack in the symbolic (abstract) model. Our original contribution is that we deal with the uniformity properties, which cannot be described using a single execution trace, while considering an unbounded number of sessions of the protocols in the presence of active and adaptive adversaries.

Original languageEnglish
Title of host publicationTrusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers
EditorsMoti Yung, Jianbiao Zhang, Zhen Yang
PublisherSpringer Verlag
Pages99-113
Number of pages15
ISBN (Print)9783319315492
DOIs
Publication statusPublished - Jan 1 2016
Event7th International Conference on the Theory, Technologies and Applications of Trusted Systems, INTRUST 2015 - Beijing, China
Duration: Dec 7 2015Dec 8 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9565
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on the Theory, Technologies and Applications of Trusted Systems, INTRUST 2015
CountryChina
CityBeijing
Period12/7/1512/8/15

Fingerprint

Multiparty Computation
Soundness
Uniformity
Attack
Secret Sharing Scheme
Trace
Acoustic waves
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhao, H., & Sakurai, K. (2016). Computational soundness of uniformity properties for multi-party computation based on LSSS. In M. Yung, J. Zhang, & Z. Yang (Eds.), Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers (pp. 99-113). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9565). Springer Verlag. https://doi.org/10.1007/978-3-319-31550-8_7

Computational soundness of uniformity properties for multi-party computation based on LSSS. / Zhao, Hui; Sakurai, Kouichi.

Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers. ed. / Moti Yung; Jianbiao Zhang; Zhen Yang. Springer Verlag, 2016. p. 99-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9565).

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

Zhao, H & Sakurai, K 2016, Computational soundness of uniformity properties for multi-party computation based on LSSS. in M Yung, J Zhang & Z Yang (eds), Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9565, Springer Verlag, pp. 99-113, 7th International Conference on the Theory, Technologies and Applications of Trusted Systems, INTRUST 2015, Beijing, China, 12/7/15. https://doi.org/10.1007/978-3-319-31550-8_7
Zhao H, Sakurai K. Computational soundness of uniformity properties for multi-party computation based on LSSS. In Yung M, Zhang J, Yang Z, editors, Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers. Springer Verlag. 2016. p. 99-113. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-31550-8_7
Zhao, Hui ; Sakurai, Kouichi. / Computational soundness of uniformity properties for multi-party computation based on LSSS. Trusted Systems - 7th International Conference, INTRUST 2015, Revised Selected Papers. editor / Moti Yung ; Jianbiao Zhang ; Zhen Yang. Springer Verlag, 2016. pp. 99-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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