EPP-DMM: An Efficient and Privacy-Protected Delegation Scheme for Matrix Multiplication

In cloud computing, outsourcing computation , as an economically promising paradigm, enables clients with limited computing resources to delegate intensive tasks to the powerful cloud server. Nevertheless, outsourcing the involved data without encryption to public brings a high risk of revealing the confidential information of the client, and no matter the returned results from server are valid or not. Due to the low trustiness of the server, it is crucial to guarantee both the security and efficiency in delegation. Motivated by those challenges, a rapidly-growing body of research mostly focuses on how to delegate mostly utilizing a feasible mechanism-verifiable computation, where the server returns results along with corresponding verification proofs. In this paper, we propose an efficient and privacyprotected delegation scheme for matrix multiplication (EPPDMM) of amortized model. EPP-DMM can realize efficient public verification together with sensitive protected data. Security analysis proves the reliability and security of our scheme, while performance evaluation presents the efficiency compared with other schemes.