article

Secure multiparty computation of approximations

Authors:

Joan Feigenbaum,

Martin J. Strauss,

Rebecca N. WrightAuthors Info & Claims

ACM Transactions on Algorithms (TALG), Volume 2, Issue 3

Pages 435 - 472

https://doi.org/10.1145/1159892.1159900

Published: 01 July 2006 Publication History

Abstract

Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely large. Furthermore, for some applications, the parties want to compute a function of their inputs securely without revealing more information than necessary. In this work, we study the question of simultaneously addressing the above efficiency and security concerns via what we call secure approximations.We start by extending standard definitions of secure (exact) computation to the setting of secure approximations. Our definitions guarantee that no additional information is revealed by the approximation beyond what follows from the output of the function being approximated. We then study the complexity of specific secure approximation problems. In particular, we obtain a sublinear-communication protocol for securely approximating the Hamming distance and a polynomial-time protocol for securely approximating the permanent and related #P-hard problems.

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Cited By

Christensen RPandey SPopovski P(2023)Semi-Private Computation of Data Similarity With Applications to Data Valuation and PricingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.325987918(1978-1988)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3259879
Boneh DBoyle ECorrigan-Gibbs HGilboa NIshai Y(2023)Arithmetic SketchingAdvances in Cryptology – CRYPTO 202310.1007/978-3-031-38557-5_6(171-202)Online publication date: 20-Aug-2023
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Choi SDachman-Soled DGordon SLiu LYerukhimovich A(2022)Secure Sampling with Sublinear CommunicationTheory of Cryptography10.1007/978-3-031-22365-5_13(348-377)Online publication date: 7-Nov-2022
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Index Terms

Secure multiparty computation of approximations
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Transactions on Algorithms

ACM Transactions on Algorithms Volume 2, Issue 3

July 2006

193 pages

ISSN:1549-6325

EISSN:1549-6333

DOI:10.1145/1159892

Issue’s Table of Contents

Copyright © 2006 ACM.

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Publication History

Published: 01 July 2006

Published in TALG Volume 2, Issue 3

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Cited By

Christensen RPandey SPopovski P(2023)Semi-Private Computation of Data Similarity With Applications to Data Valuation and PricingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.325987918(1978-1988)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3259879
Boneh DBoyle ECorrigan-Gibbs HGilboa NIshai Y(2023)Arithmetic SketchingAdvances in Cryptology – CRYPTO 202310.1007/978-3-031-38557-5_6(171-202)Online publication date: 20-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-38557-5_6
Choi SDachman-Soled DGordon SLiu LYerukhimovich A(2022)Secure Sampling with Sublinear CommunicationTheory of Cryptography10.1007/978-3-031-22365-5_13(348-377)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-22365-5_13
Meng XPapadopoulos DOprea ATriandopoulos NZhang Yvan Dijk M(2021)Private Hierarchical Clustering and Efficient ApproximationProceedings of the 2021 on Cloud Computing Security Workshop10.1145/3474123.3486760(3-20)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3474123.3486760
Gkoulalas-Divanis AVatsalan DKarapiperis DKantarcioglu M(2021)Modern Privacy-Preserving Record Linkage Techniques: An OverviewIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.311402616(4966-4987)Online publication date: 2021
https://doi.org/10.1109/TIFS.2021.3114026
Choi SDachman-soled DKulkarni MYerukhimovich A(2020)Differentially-Private Multi-Party Sketching for Large-Scale StatisticsProceedings on Privacy Enhancing Technologies10.2478/popets-2020-00472020:3(153-174)Online publication date: 17-Aug-2020
https://doi.org/10.2478/popets-2020-0047
Kornaropoulos EEfstathopoulos P(2019)The Case of Adversarial Inputs for Secure Similarity Approximation Protocols2019 IEEE European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP.2019.00027(247-262)Online publication date: Jun-2019
https://doi.org/10.1109/EuroSP.2019.00027
Doriguello JMontanaro A(2019)Quantum sketching protocols for Hamming distance and beyondPhysical Review A10.1103/PhysRevA.99.06233199:6Online publication date: 24-Jun-2019
https://doi.org/10.1103/PhysRevA.99.062331
Li CZhou PXiong LWang QWang T(2018)Differentially Private Distributed Online LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.279438430:8(1440-1453)Online publication date: 1-Aug-2018
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Berger BBrakerski Z(2018)Zero-Knowledge Protocols for Search ProblemsSecurity and Cryptography for Networks10.1007/978-3-319-98113-0_16(292-309)Online publication date: 5-Sep-2018
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