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Unbounded Multi-party Computation from Learning with Errors

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Advances in Cryptology – EUROCRYPT 2021 (EUROCRYPT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12697))

Abstract

We consider the problem of round-optimal unbounded MPC: in the first round, parties publish a message that depends only on their input. In the second round, any subset of parties can jointly and securely compute any function f over their inputs in a single round of broadcast. We do not impose any a-priori bound on the number of parties nor on the size of the functions that can be computed.

Our main result is a semi-honest two-round protocol for unbounded MPC in the plain model from the hardness of the standard learning with errors (LWE) problem. Prior work in the same setting assumes the hardness of problems over bilinear maps. Thus, our protocol is the first example of unbounded MPC that is post-quantum secure.

The central ingredient of our protocol is a new scheme of attribute-based secure function evaluation (AB-SFE) with public decryption. Our construction combines techniques from the realm of homomorphic commitments with delegation of lattice basis. We believe that such a scheme may find further applications in the future.

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Notes

  1. 1.

    Note that we could have merged the \(\mathsf {Setup}\) and the \(\mathsf {KeyGen}\) algorithms in a single subroutine, however we refrained to do so in order to match the original syntax from [27].

  2. 2.

    In the technical sections, instead of using the terms SampleLeft and SampleRight, we use the algorithm GenSamplePre that captures the functionality of both these algorithms.

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Author information

Authors and Affiliations

  1. University of California, Santa Barbara, CA, USA

    Prabhanjan Ananth

  2. Johns Hopkins University, Baltimore, MD, USA

    Abhishek Jain & Zhengzhong Jin

  3. Max Planck Institute for Security and Privacy, Bochum, Germany

    Giulio Malavolta

Authors
  1. Prabhanjan Ananth
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  2. Abhishek Jain
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  3. Zhengzhong Jin
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  4. Giulio Malavolta
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Editors and Affiliations

  1. Inria, Paris, France

    Anne Canteaut

  2. UCLouvain, Louvain-la-Neuve, Belgium

    François-Xavier Standaert

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Ananth, P., Jain, A., Jin, Z., Malavolta, G. (2021). Unbounded Multi-party Computation from Learning with Errors. In: Canteaut, A., Standaert, FX. (eds) Advances in Cryptology – EUROCRYPT 2021. EUROCRYPT 2021. Lecture Notes in Computer Science(), vol 12697. Springer, Cham. https://doi.org/10.1007/978-3-030-77886-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-77886-6_26

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