[Resource Topic] 2022/1207: Attaining GOD Beyond Honest Majority With Friends and Foes

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Attaining GOD Beyond Honest Majority With Friends and Foes

Authors: Aditya Hegde, Nishat Koti, Varsha Bhat Kukkala, Shravani Patil, Arpita Patra, Protik Paul


In the classical notion of multiparty computation (MPC), an honest party learning private inputs of others, either as a part of protocol specification or due to a malicious party’s unspecified messages, is not considered a potential breach. Several works in the literature exploit this seemingly minor loophole to achieve the strongest security of guaranteed output delivery via a trusted third party, which nullifies the purpose of MPC. Alon et al. (CRYPTO 2020) presented the notion of Friends and Foes (\mathtt{FaF}) security, which accounts for such undesired leakage towards honest parties by modelling them as semi-honest (friends) who do not collude with malicious parties (foes). With real-world applications in mind, it’s more realistic to assume parties are semi-honest rather than completely honest, hence it is imperative to design efficient protocols conforming to the \mathtt{FaF} security model.

Our contributions are not only motivated by the practical viewpoint, but also consider the theoretical aspects of \mathtt{FaF} security. We prove the necessity of semi-honest oblivious transfer for \mathtt{FaF}-secure protocols with optimal resiliency. On the practical side, we present QuadSquad, a ring-based 4PC protocol, which achieves fairness and GOD in the \mathtt{FaF} model, with an optimal corruption of 1 malicious and 1 semi-honest party. QuadSquad is, to the best of our knowledge, the first practically efficient \mathtt{FaF} secure protocol with optimal resiliency. Its performance is comparable to the state-of-the-art dishonest majority protocols while improving the security guarantee from abort to fairness and GOD. Further, QuadSquad elevates the security by tackling a stronger adversarial model over the state-of-the-art honest-majority protocols, while offering a comparable performance for the input-dependent computation. We corroborate these claims by benchmarking the performance of QuadSquad. We also consider the application of liquidity matching that deals with highly sensitive financial transaction data, where \mathtt{FaF} security is apt. We design a range of \mathtt{FaF} secure building blocks to securely realize liquidity matching as well as other popular applications such as privacy-preserving machine learning (PPML). Inclusion of these blocks makes QuadSquad a comprehensive framework.

ePrint: https://eprint.iacr.org/2022/1207

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