LOW COMPLEXITY SECURE P-TENSOR PRODUCT COMPRESSED SENSING RECONSTRUCTION OUTSOURCING AND IDENTITY AUTHENTICATION IN CLOUD

Although P-tensor product compressed sensing (PTP-CS) breaks the strict dimension matching restriction between measurement matrix and signal in multiplication, it still faces the huge resource consumption problems of the frequent update and transmission of measurement matrix and signal reconstruction. Accordingly, we design a public measurement matrix and utilize the cloud to solve the PTP-CS reconstruction (PTP-CSR) task under privacy protection. Specifically, we propose a low complexity and secure PTP-CSR outsourcing model to protect the signal privacy, and further introduce user authentication and data verification services. In our model, the client samples the signal based on PTP-CS and uploads the encrypted measurement to the cloud. The cloud further encrypts the data asymmetrically for storage security and user management. After receiving the request, the cloud authenticates the user's identity. Once successful, the cloud processes the encrypted PTP-CSR task and returns result to users. More importantly, we provide result verification approach for users. Our experimental results demonstrate the privacy protection of the signal and the effectiveness of the proposed model.