research-article

Secure Graph Analysis at Scale

Authors:

Toshinori Araki,

Hanan Rosemarin,

Hikaru TsuchidaAuthors Info & Claims

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Pages 610 - 629

https://doi.org/10.1145/3460120.3484560

Published: 13 November 2021 Publication History

Abstract

We present a highly-scalable secure computation of graph algorithms, which hides all information about the topology of the graph or other input values associated with nodes or edges. The setting is where all nodes and edges of the graph are secret-shared between multiple servers, and a secure computation protocol is run between these servers. While the method is general, we demonstrate it in a 3-server setting with an honest majority, with either semi-honest security or full security. A major technical contribution of our work is replacing the usage of secure sort protocols with secure shuffles, which are much more efficient. Full security against malicious behavior is achieved by adding an efficient verification for the shuffle operation, and computing circuits using fully secure protocols. We demonstrate the applicability of this technology by implementing two major algorithms: computing breadth-first search (BFS), which is also useful for contact tracing on private contact graphs, and computing maximal independent set (MIS). We implement both algorithms, with both semi-honest and full security, and run them within seconds on graphs of millions of elements.

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Index Terms

Secure Graph Analysis at Scale
1. Security and privacy
  1. Cryptography
  2. Security services
    1. Privacy-preserving protocols

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cover image ACM Conferences

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

November 2021

3558 pages

ISBN:9781450384544

DOI:10.1145/3460120

General Chairs:
Yongdae Kim
KAIST, Republic of Korea
,
Jong Kim
POSTECH, Republic of Korea
,
Program Chairs:
Giovanni Vigna
University of California, Santa Barbara / VMware, USA
,
Elaine Shi
Carnegie Mellon University, USA

Copyright © 2021 ACM.

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Published: 13 November 2021

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November 15 - 19, 2021

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

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https://doi.org/10.1587/transinf.2024PAP0006
Attema Tvan Baarsen Avan den Berg SCapitão PDunning VKohl L(2024)Communication-Efficient Multi-Party Computation for RMS ProgramsIACR Communications in Cryptology10.62056/ab0lmp-3yOnline publication date: 8-Jul-2024
https://doi.org/10.62056/ab0lmp-3y
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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Ehrmanntraut VMeyer UAyday EVaidya J(2024)Privacy-Preserving Breadth-First-Search and Maximal FlowProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695041(73-97)Online publication date: 20-Nov-2024
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