Towards privacy-preserving compressed sensing reconstruction in cloud

Abstract

Compressed sensing is widely used in various fields. Its reconstruction process is highly complex and time-consuming. For resource-constrained Internet of Things (IoT) devices, there are usually not enough computational and storage resources to handle it. The prevalent solution to this problem involves secure outsourcing the compressed sensing reconstruction task to the cloud. Nonetheless, existing privacy-preserving compressed sensing reconstruction protocols are primarily designed based on linear programming, but not applicable to other reconstruction methods. In these protocols, the computational cost on the user and the cloud is still high. To tackle these issues, we design a privacy-preserving compressed sensing reconstruction protocol specifically tailored for IoT applications. Different from existing works, our proposed protocol can be applicable to all reconstruction algorithms. It allows the cloud flexibly choose the appropriate signal reconstruction method. The proposed protocol directly encrypts the reconstruction problem. In the ciphertext state, the reconstruction problem is transformed into other forms of the problem for solving. We use a signal obfuscation method for encryption in the proposed protocol. The user no longer needs to perform matrix multiplication calculations for encryption, saving a lot of computational resources. Our proposed protocol not only ensures the client privacy by preventing data leakage to cloud but also effectively reduces computational complexity for both the user and the cloud. Finally, we theoretically analyze the correctness and security of the protocol and experimentally verify its feasibility.