Welcome to the resource topic for 2022/1207
Title:
Attaining GOD Beyond Honest Majority With Friends and Foes
Authors: Aditya Hegde, Nishat Koti, Varsha Bhat Kukkala, Shravani Patil, Arpita Patra, Protik Paul
Abstract: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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