Secure multiparty computation (
MPC) addresses the challenge of evaluating functions on secret inputs without compromising their privacy. A central question in multiparty computation is to understand the amount of communication needed to securely evaluate a circuit of size s. In this work, we revisit this fundamental question in the setting of information-theoretically secure
MPC in the correlated randomness model, where a trusted dealer distributes correlated random coins, independent of the inputs, to all parties before the start of the protocol. This setting is of strong theoretical interest, and has led to the most practically efficient
MPC protocols known to date.
In the setting of secure multiparty computation, a set of parties with private inputs wish to compute some function of their inputs without revealing anything but their output. Over the last decade, the efficiency of secure two-party computation has advanced in leaps and bounds, with speedups of some orders of magnitude, making it fast enough to be of use in practice. In contrast, progress on the case of multiparty computation (with more than two parties) has been much slower, with very little work being done. Currently, the only implemented efficient multiparty protocol has many rounds of communication (linear in the depth of the circuit being computed) and thus is not suited for Internet-like settings where latency is not very low. In this paper, we construct highly efficient constant-round protocols for the setting of multiparty computation for semi-honest adversaries. Our protocols work by constructing a multiparty garbled circuit, as proposed in BMR (Beaver et al., STOC 1990). Our first protocol uses oblivious transfer and constitutes the first concretely-efficient constant-round multiparty protocol for the case of no honest majority. Our second protocol uses BGW, and is significantly more efficient than the FairplayMP protocol (Ben-David et al., CCS 2008) that also uses BGW.
Abstract Secure Multiparty Computation (MPC) is a cryptography technique that allows multiple parties to securely perform operations on their private inputs without revealing them. It is used in various applications, such as data aggregation in the cloud, IoT, etc., where data privacy is the prime concern. Many researchers have explored ways to develop secure protocols with minimal risk of data leakage. Many of these classical secure MPC protocols depend on classical random number generators. However, advances in quantum information processing are putting classical key distribution methods at risk. Traditional summation protocols adopt an
((n,n)) threshold approach, requiring the participation of all
(n) players to compute the sum securely. However, the proposed quantum secure multiparty computation based on secure summation and QKD (QSMPC-SSQKD) introduces a generalised quantum secure multiparty summation protocol. The proposed protocol utilises entanglement, a fundamental property of quantum mechanics, to facilitate safe key exchange, thereby ensuring robust security measures. We use a quantum random number generator (QRNG) to generate true random numbers, ensuring privacy preservation. The protocol achieves a per-bit entropy level of 0.708 through QRNG, which provides strong randomness. The security of the proposed protocol is validated using the CHSCH test (
(B≈2.8)), ensuring that any attempt to eavesdrop on the system can be detected, and the complete process will be rescheduled. Compared to some similar protocols, the proposed QSMPC-SSQKD protocol provides higher randomness and lower quantum circuit cost without using any trusted third party (TTP). This improvement enhances the protocol’s efficiency and practicality in preventing data leakage during computation.