With the prevalence of cloud computing, the resource constrained clients are trended to outsource their computation-intensive tasks to the cloud server. Although outsourcing computation paradigm brings many benefits for both clients and cloud server, it causes some security challenges. In this paper, we focus on the outsourcing computation of matrix multiplication, and propose a new publicly verifiable computation scheme for batch matrix multiplication. Different from traditional matrix computation outsourcing model, the outsourcing task of our scheme is to compute MX i for group of clients, where X i is a private matrix chosen by different clients and M is a public matrix given by a data center beforehand. Based on the two techniques of privacy-preserving matrix transformation and matrix digest, our scheme can protect the secrecy of the client's private matrix X i and dramatically reduce the computation cost in both the key generation and the computing phases. Security analysis shows that the proposed scheme can achieve the desired security properties under the co-computational Diffie-Hellman assumption. We also provide the experimental evaluation that demonstrates the efficiency of our scheme.

New publicly verifiable computation for batch matrix multiplication

Castiglione A.;
2019-01-01

Abstract

With the prevalence of cloud computing, the resource constrained clients are trended to outsource their computation-intensive tasks to the cloud server. Although outsourcing computation paradigm brings many benefits for both clients and cloud server, it causes some security challenges. In this paper, we focus on the outsourcing computation of matrix multiplication, and propose a new publicly verifiable computation scheme for batch matrix multiplication. Different from traditional matrix computation outsourcing model, the outsourcing task of our scheme is to compute MX i for group of clients, where X i is a private matrix chosen by different clients and M is a public matrix given by a data center beforehand. Based on the two techniques of privacy-preserving matrix transformation and matrix digest, our scheme can protect the secrecy of the client's private matrix X i and dramatically reduce the computation cost in both the key generation and the computing phases. Security analysis shows that the proposed scheme can achieve the desired security properties under the co-computational Diffie-Hellman assumption. We also provide the experimental evaluation that demonstrates the efficiency of our scheme.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4810942
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