Privacy-Preserving Publicly Verifiable Outsourced Distributed Computation Scheme for Matrix Multiplication

Abstract

Publicly verifiable outsourced computation (PVC) facilitates the data owner to outsource some computation-intensive tasks to the powerful but untrusted cloud server, while enabling any client to check the integrity of results with little cost. Matrix multiplication is a fundamental operation in mathematics, which is widely used in many real-world applications. In this paper, we focus on PVC for matrix multiplication (PVC2M) and propose a new primitive called privacy-preserving publicly verifiable outsourced distributed computation scheme (PPVDC) for matrix multiplication. Different from the existing PVC2M solutions, our proposed scheme offers higher efficiency and reliability, where the computation is jointly calculated by multiple workers. In such a distributed setting, the computation result can be recovered if the number of workers who perform the computation honestly is no less than threshold. Besides, another technical highlight is to enhance privacy. Even though all workers are corrupted and may collude, they are unable to obtain any knowledge about the matrix $M$ outsourced by the data owner and the vector $x$ issued by the client at the end of the protocol. Security analysis demonstrates that our proposed PPVDC scheme can meet the desired security requirements under the computational Diffie-Hellman assumption. The detailed performance analysis and experimental evaluation further validate the efficiency of our scheme.