short-paper

Domain-polymorphic language for privacy-preserving applications

Authors:

Jaak RandmetsAuthors Info & Claims

PETShop '13: Proceedings of the First ACM workshop on Language support for privacy-enhancing technologies

Pages 23 - 26

https://doi.org/10.1145/2517872.2517875

Published: 04 November 2013 Publication History

Abstract

We present SecreC, a programming language for specifying privacy-preserving applications using a mix of techniques for secure multiparty computation. Building on the concept of protection domain as an abstraction of resources used to ensure the privacy of data, the SecreC language allows the specification of protection domains for different pieces of data, and the specification of the computation in domain-polymorphic manner. We have implemented the compiler for the language, integrated it with the existing SMC framework Sharemind, and are currently using it for new privacy-preserving applications.

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Cited By

Belorgey MCarpov SDeforth KJetchev DSae-Tang AVuille MGama NKatz JLeontiadis IMohammadi M(2023)Manticore: A Framework for Efficient Multiparty Computation Supporting Real Number and Boolean ArithmeticJournal of Cryptology10.1007/s00145-023-09464-436:3Online publication date: 11-Jul-2023
https://doi.org/10.1007/s00145-023-09464-4
Willemson J(2020)How Not to Use a Privacy-Preserving Computation Platform: Case Study of a Voting ApplicationComputer Security10.1007/978-3-030-42048-2_8(111-121)Online publication date: 22-Feb-2020
https://doi.org/10.1007/978-3-030-42048-2_8
Chandran NGupta DRastogi ASharma RTripathi S(2019)EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning2019 IEEE European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP.2019.00043(496-511)Online publication date: Jun-2019
https://doi.org/10.1109/EuroSP.2019.00043
Show More Cited By

Index Terms

Domain-polymorphic language for privacy-preserving applications
1. Security and privacy
  1. Cryptography
  2. Systems security
    1. Operating systems security
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Polymorphism

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Information

Published In

cover image ACM Conferences

PETShop '13: Proceedings of the First ACM workshop on Language support for privacy-enhancing technologies

November 2013

36 pages

ISBN:9781450324892

DOI:10.1145/2517872

Program Chairs:
Martin Franz
Deutsche Bank, Germany
,
Andreas Holzer
Vienna University of Technology, Austria
,
Rupak Majumdar
MPI SWS Kaiserslautern, Germany
,
Bryan Parno
Microsoft Research, USA
,
Helmut Veith
Vienna University of Technology, Austria

Copyright © 2013 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 the author(s) 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: 04 November 2013

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Short-paper

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CCS'13

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SIGSAC

CCS'13: 2013 ACM SIGSAC Conference on Computer and Communications Security

November 4, 2013

Berlin, Germany

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PETShop '13 Paper Acceptance Rate 7 of 8 submissions, 88%;

Overall Acceptance Rate 7 of 8 submissions, 88%

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CCS '24

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ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

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Cited By

Belorgey MCarpov SDeforth KJetchev DSae-Tang AVuille MGama NKatz JLeontiadis IMohammadi M(2023)Manticore: A Framework for Efficient Multiparty Computation Supporting Real Number and Boolean ArithmeticJournal of Cryptology10.1007/s00145-023-09464-436:3Online publication date: 11-Jul-2023
https://doi.org/10.1007/s00145-023-09464-4
Willemson J(2020)How Not to Use a Privacy-Preserving Computation Platform: Case Study of a Voting ApplicationComputer Security10.1007/978-3-030-42048-2_8(111-121)Online publication date: 22-Feb-2020
https://doi.org/10.1007/978-3-030-42048-2_8
Chandran NGupta DRastogi ASharma RTripathi S(2019)EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning2019 IEEE European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP.2019.00043(496-511)Online publication date: Jun-2019
https://doi.org/10.1109/EuroSP.2019.00043
Liu XYi X(2019)Privacy-Preserving Collaborative Medical Time Series Analysis Based on Dynamic Time WarpingComputer Security – ESORICS 201910.1007/978-3-030-29962-0_21(439-460)Online publication date: 15-Sep-2019
https://doi.org/10.1007/978-3-030-29962-0_21
Baldimtsi FPapadopoulos DPapadopoulos SScafuro ATriandopoulos N(2017)Server-Aided Secure Computation with Off-line PartiesComputer Security – ESORICS 201710.1007/978-3-319-66402-6_8(103-123)Online publication date: 12-Aug-2017
https://doi.org/10.1007/978-3-319-66402-6_8
Pullonen PSiim S(2015)Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point ComputationsFinancial Cryptography and Data Security10.1007/978-3-662-48051-9_13(172-183)Online publication date: 5-Sep-2015
https://doi.org/10.1007/978-3-662-48051-9_13
Bogdanov DLaur STalviste R(2014)A Practical Analysis of Oblivious Sorting Algorithms for Secure Multi-party ComputationSecure IT Systems10.1007/978-3-319-11599-3_4(59-74)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11599-3_4
Bogdanov DKamm LLaur SPruulmann-Vengerfeldt PTalviste RWillemson J(2014)Privacy-Preserving Statistical Data Analysis on Federated DatabasesPrivacy Technologies and Policy10.1007/978-3-319-06749-0_3(30-55)Online publication date: 2014
https://doi.org/10.1007/978-3-319-06749-0_3

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