Secure Multi-Party Computation (SMPC) aims to enable computation on sensitive data without revealing the individual inputs of the participants. Traditional SMPC protocols often suffer from significant computational overhead, particularly in terms of communication complexity. This paper proposes a novel approach to SMPC leveraging tensor network techniques inspired by quantum computation. We demonstrate that tensor networks can efficiently represent and manipulate the necessary data structures for SMPC protocols, leading to a substantial reduction in communication overhead and improved scalability. Our method utilizes the inherent structure of tensor networks to minimize the number of exchanges required during computation, effectively addressing a key bottleneck in existing SMPC implementations. We outline the theoretical foundations and present a framework for designing SMPC protocols using tensor networks. This work represents a significant step towards practical and efficient SMPC solutions, particularly for large-scale datasets and complex computations. The key contribution lies in the application of quantum-inspired techniques to solve classical computational problems, specifically within the context of secure computation.Secure Multi-Party Computation (SMPC) aims to enable computation on sensitive data without revealing the individual inputs of the participants. Traditional SMPC protocols often suffer from significant computational overhead, particularly in terms of communication complexity. This paper proposes a novel approach to SMPC leveraging tensor network techniques inspired by quantum computation. We demonstrate that tensor networks can efficiently represent and manipulate the necessary data structures for SMPC protocols, leading to a substantial reduction in communication overhead and improved scalability. Our method utilizes the inherent structure of tensor networks to minimize the number of exchanges required during computation, effectively addressing a key bottleneck in existing SMPC implementations. We outline the theoretical foundations and present a framework for designing SMPC protocols using tensor networks. This work represents a significant step towards practical and efficient SMPC solutions, particularly for large-scale datasets and complex computations. The key contribution lies in the application of quantum-inspired techniques to solve classical computational problems, specifically within the context of secure computation.

Sep 3, 20260 citations

Efficient Secure Multi-party Computation

Martin Hirt, Ueli M. Maurer, Bartosz Przydatek

Lecture notes in computer scienceJan 1, 2000123 citations

Asynchronous Multi-Party Computation in Large Networks

Varsha Dani, Valerie King, Mahnush Movahedi +2 more

We describe scalable protocols for solving the secure multi-party computation (MPC) problem among a large number of parties. We consider both the synchronous and the asynchronous communication models. In the synchronous setting, our protocol is secure against a static malicious adversary corrupting less than a $1/3$ fraction of the parties. In the asynchronous setting, we allow the adversary to corrupt less than a $1/8$ fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with $m$ gates, both of our protocols require each party to send a number of field elements and perform an amount of computation that is $\tilde{O}(m/n + \sqrt n)$ . We also show that our protocols provide perfect and universally-composable security. To achieve our asynchronous MPC result, we define the \emph{threshold counting problem} and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of $O(\log{n})$ , and can also be used for designing other load-balanced applications in the asynchronous communication model.We describe scalable protocols for solving the secure multi-party computation (MPC) problem among a large number of parties. We consider both the synchronous and the asynchronous communication models. In the synchronous setting, our protocol is secure against a static malicious adversary corrupting less than a $1/3$ fraction of the parties. In the asynchronous setting, we allow the adversary to corrupt less than a $1/8$ fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with $m$ gates, both of our protocols require each party to send a number of field elements and perform an amount of computation that is $\tilde{O}(m/n + \sqrt n)$. We also show that our protocols provide perfect and universally-composable security. To achieve our asynchronous MPC result, we define the \emph{threshold counting problem} and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of $O(\log{n})$, and can also be used for designing other load-balanced applications in the asynchronous communication model.

arXiv:1203.0289Mar 1, 20120 citations

1/3

fraction of the parties. In the asynchronous setting, we allow the adversary to corrupt less than a

1/8

fraction of parties. For any deterministic function that can be computed by an arithmetic circuit with

m

gates, both of our protocols require each party to send a number of field elements and perform an amount of computation that is

\tilde{O}(m/n + \sqrt n)

. We also show that our protocols provide perfect and universally-composable security. To achieve our asynchronous MPC result, we define the \emph{threshold counting problem} and present a distributed protocol to solve it in the asynchronous setting. This protocol is load balanced, with computation, communication and latency complexity of

O(\log{n})

, and can also be used for designing other load-balanced applications in the asynchronous communication model.