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Attaining GOD Beyond Honest Majority with Friends and Foes

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Advances in Cryptology – ASIACRYPT 2022 (ASIACRYPT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13791))

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 (\(\texttt{FaF}\)) 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 \(\texttt{FaF}\) security model.

Our contributions are not only motivated by the practical viewpoint, but also consider the theoretical aspects of \(\texttt{FaF}\) security. We prove the necessity of semi-honest oblivious transfer for \(\texttt{FaF}\)-secure protocols with optimal resiliency. On the practical side, we present QuadSquad, a ring-based 4PC protocol, which achieves fairness and GOD in the \(\texttt{FaF}\) 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 \(\texttt{FaF}\) 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 consider the application of liquidity matching that deals with sensitive financial transaction data, where \(\texttt{FaF}\) security is apt. We design a range of \(\texttt{FaF}\) secure building blocks to securely realize liquidity matching as well as other popular applications such as privacy-preserving machine learning. Inclusion of these blocks makes QuadSquad a comprehensive framework.

Full version available at https://eprint.iacr.org/2022/1207.pdf

A. Hegde—Work done while at International Institute of Information Technology Bangalore.

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Notes

  1. 1.

    As mentioned in Sect. 1.1, SHE offers an alternative to OT. However, relying on the heels of recent interesting work on OT [72] and the huge effort on improving OT in the last decade [19, 52], we opt for OT based approach. Translating our approach in the SHE regime is left for future exploration.

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Acknowledgements

Arpita Patra would like to acknowledge financial support from DST National Mission on Interdisciplinary Cyber-Physical Systems (NM-CPS) 2020–2025 and SERB MATRICS (Theoretical Sciences) Grant 2020–2023. Varsha Bhat Kukkala would like to acknowledge financial support from National Security Council, India. Nishat Koti would like to acknowledge support from Centre for Networked Intelligence (a Cisco CSR initiative) at the Indian Institute of Science, Bengaluru. Shravani Patil would like to acknowledge financial support from DST National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS) 2020–2025. The authors would also like to acknowledge the support from Google Cloud for benchmarking.

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  1. Johns Hopkins University, Baltimore, USA

    Aditya Hegde

  2. Indian Institute of Science, Bangalore, Bengaluru, India

    Nishat Koti, Varsha Bhat Kukkala, Shravani Patil, Arpita Patra & Protik Paul

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Hegde, A., Koti, N., Kukkala, V.B., Patil, S., Patra, A., Paul, P. (2022). Attaining GOD Beyond Honest Majority with Friends and Foes. In: Agrawal, S., Lin, D. (eds) Advances in Cryptology – ASIACRYPT 2022. ASIACRYPT 2022. Lecture Notes in Computer Science, vol 13791. Springer, Cham. https://doi.org/10.1007/978-3-031-22963-3_19

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