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We study efficient public randomness generation protocols in the PASSO (PArties Speak Sequentially Once) model for multi-party computation (MPC). \(\text {PASSO}\) is a variation of traditional MPC where n parties are executed in sequence and each party “speaks” only once, broadcasting and sending secret messages only to parties further down the line. Prior results in this setting include information-theoretic protocols in which the computational complexity scales exponentially with the number of corruptions t (CRYPTO 2022), as well as more efficient computationally-secure protocols either assuming a trusted setup phase or DDH (FC 2024). Moreover, these works only consider security against static adversaries. In this work, we focus on computational security against adaptive adversaries and from minimal assumptions, and improve on the works mentioned above in several ways: We complement these results by studying lower bounds for randomness generation protocols in the computational setting.

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Efficient Distributed Randomness Generation from Minimal Assumptions Where PArties Speak Sequentially Once

  • 会议论文

  • 发表日期：

    2025年

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  • Chen-Da Liu-Zhang,
  • Elisaweta Masserova,
  • João Ribeiro,
  • Pratik Soni,
  • Sri AravindaKrishnan Thyagarajan

摘要

We study efficient public randomness generation protocols in the PASSO (PArties Speak Sequentially Once) model for multi-party computation (MPC). \(\text {PASSO}\) is a variation of traditional MPC where n parties are executed in sequence and each party “speaks” only once, broadcasting and sending secret messages only to parties further down the line. Prior results in this setting include information-theoretic protocols in which the computational complexity scales exponentially with the number of corruptions t (CRYPTO 2022), as well as more efficient computationally-secure protocols either assuming a trusted setup phase or DDH (FC 2024). Moreover, these works only consider security against static adversaries. In this work, we focus on computational security against adaptive adversaries and from minimal assumptions, and improve on the works mentioned above in several ways: We complement these results by studying lower bounds for randomness generation protocols in the computational setting.