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Secure Multiparty Computation from SGX

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

  • Bernardo Portela
  • Manuel B M Barbosa
  • Ferdinand Brasser
  • Bernardo Portela
  • Ahmad-Reza Sadeghi
  • Guillaume Scerri
  • Bogdan Warinschi
Original languageEnglish
Title of host publicationFinancial Cryptography and Data Security 2017
Subtitle of host publicationTwenty-First International Conference, April 3–7, 2017, The Palace Hotel, Malta
Publisher or commissioning bodyInternational Financial Cryptography Association
DateAccepted/In press - 13 Jan 2017
DatePublished (current) - 5 Apr 2017

Abstract

In this paper we show how Isolated Execution Environments (IEE) offered by novel commodity hardware such as Intel’s SGX provide a new path to constructing general secure multiparty computation (MPC) protocols. Our protocol is intuitive and elegant: it uses code within an IEE to play the role of a trusted third party (TTP), and the attestation guarantees of SGX to bootstrap secure communications between participants and the TTP. The load of communications and computations on participants only depends on the size of each party’s inputs and outputs and is thus small and independent from the intricacies of the functionality to be computed. The remaining computational load– essentially that of computing the functionality – is moved to an untrusted party running an IEE-enabled machine, an attractive feature for Cloud-based scenarios.

Our rigorous modular security analysis relies on the novel notion of labeled attested computation which we put forth in this paper. This notion is a convenient abstraction of the kind of attestation guarantees one can obtain from trusted hardware in multi-user scenarios.

Finally, we present an extensive experimental evaluation of our solution on SGXenabled hardware. Our implementation is open-source and it is functionality agnostic: it can be used to securely outsource to the Cloud arbitrary off-the-shelf collaborative software, such as the one employed on financial data applications, enabling secure collaborative execution over private inputs provided by multiple parties.

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via http://fc17.ifca.ai/preproceedings/paper_89.pdf

    Accepted author manuscript, 423 KB, PDF-document

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