research-article

High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority

Authors:

Toshinori Araki,

Yehuda Lindell,

Kazuma OharaAuthors Info & Claims

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Pages 805 - 817

https://doi.org/10.1145/2976749.2978331

Published: 24 October 2016 Publication History

Abstract

In this paper, we describe a new information-theoretic protocol (and a computationally-secure variant) for secure three-party computation with an honest majority. The protocol has very minimal computation and communication; for Boolean circuits, each party sends only a single bit for every AND gate (and nothing is sent for XOR gates). Our protocol is (simulation-based) secure in the presence of semi-honest adversaries, and achieves privacy in the client/server model in the presence of malicious adversaries. On a cluster of three 20-core servers with a 10Gbps connection, the implementation of our protocol carries out over 1.3 million AES computations per second, which involves processing over 7 billion gates per second. In addition, we developed a Kerberos extension that replaces the ticket-granting-ticket encryption on the Key Distribution Center (KDC) in MIT-Kerberos with our protocol, using keys/ passwords that are shared between the servers. This enables the use of Kerberos while protecting passwords. Our implementation is able to support a login storm of over 35,000 logins per second, which suffices even for very large organizations. Our work demonstrates that high-throughput secure computation is possible on standard hardware.

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Cao WShen WQin JLin H(2025)Privacy-preserving and verifiable convolution neural network inference and training in cloud computingFuture Generation Computer Systems10.1016/j.future.2024.107560164(107560)Online publication date: Mar-2025
https://doi.org/10.1016/j.future.2024.107560
Mu GZhang HLin JKong F(2025)SMCD: Privacy-preserving deep learning based malicious code detectionComputers & Security10.1016/j.cose.2024.104226150(104226)Online publication date: Mar-2025
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Index Terms

High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority
1. Theory of computation
  1. Computational complexity and cryptography
    1. Cryptographic protocols

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Published In

cover image ACM Conferences

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

1924 pages

ISBN:9781450341394

DOI:10.1145/2976749

General Chairs:
Edgar Weippl
SBA Research, Austria
,
Stefan Katzenbeisser
TU Darmstadt, CYSEC, Germany
,
Program Chairs:
Christopher Kruegel
University of California, Santa Barbara, USA
,
Andrew Myers
Cornell University, USA
,
Shai Halevi
IBM Research, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

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European Research Council

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CCS'16

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SIGSAC

CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security

October 24 - 28, 2016

Vienna, Austria

Acceptance Rates

CCS '16 Paper Acceptance Rate 137 of 831 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cao WShen WQin JLin H(2025)Privacy-preserving and verifiable convolution neural network inference and training in cloud computingFuture Generation Computer Systems10.1016/j.future.2024.107560164(107560)Online publication date: Mar-2025
https://doi.org/10.1016/j.future.2024.107560
Mu GZhang HLin JKong F(2025)SMCD: Privacy-preserving deep learning based malicious code detectionComputers & Security10.1016/j.cose.2024.104226150(104226)Online publication date: Mar-2025
https://doi.org/10.1016/j.cose.2024.104226
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Breuer MHein PPompe LMeyer UWetzel S(2024)Prioritization and exchange chains in privacy-preserving kidney exchangeJournal of Computer Security10.3233/JCS-23001232:4(349-404)Online publication date: 26-Aug-2024
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