International Association
for Cryptologic Research

A homomorphic secret sharing (HSS) scheme allows a client to delegate a computation to a group of untrusted servers while achieving input privacy as long as at least one server is honest. In recent years, many HSS schemes have been constructed that have, in turn, found numerous applications to cryptography. Prior work on HSS focuses on the setting where the servers are semi-honest. In this work we lift HSS to the setting of malicious evaluators. We propose the notion of *HSS with verifiable evaluation* (ve-HSS) that guarantees correctness of output *even when all the servers are corrupted*. ve-HSS retains all the attractive features of HSS and adds the new feature of succinct (public) verification of output. We present *black-box* constructions of ve-HSS by devising generic transformations for semi-honest HSS schemes (with negligible error). This provides a new non-interactive method for verifiable and private outsourcing of computation.

We study secure multi-party computation (MPC) protocols for branching circuits that contain multiple sub-circuits (i.e., branches) and the output of the circuit is that of single ``active'' branch. Crucially, the identity of the active branch must remain hidden from the protocol participants. While such circuits can be securely computed by evaluating each branch and then multiplexing the output, such an approach incurs a communication cost linear in the size of the entire circuit. To alleviate this, a series of recent works have investigated the problem of reducing the communication cost of branching executions inside MPC (without relying on fully homomorphic encryption). Most notably, the stacked garbling paradigm [Heath and Kolesnikov, CRYPTO'20] yields garbled circuits for branching circuits whose size only depends on the size of the largest branch. Presently, however, it is not known how to obtain similar communication improvements for secure computation involving {\em more than two parties}. In this work, we provide a generic framework for branching multi-party computation that supports {\em any number of parties}. The communication complexity of our scheme is proportional to the size of the largest branch and the computation is linear in the size of the entire circuit. We provide an implementation and benchmarks to demonstrate practicality of our approach.

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 {\it 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.