A Secure Data Assimilation for Large-Scale Sensor Networks Using an Untrusted Cloud

Cloud computing technologies (CCTs) enable a large-scale sensor network (LSN) to outsource the computations of data assimilation to improve its performance. However, the cyber-physical nature of cloud-enabled LSNs (CE-LSNs) introduces new challenges. Outsourcing the computations to an untrusted cloud may expose the privacy of the sensing data. To address the security issues, we proposed a secure approach to achieve data confidentiality in the outsourcing process. We develop our mechanism by combining a conventional homomorphic encryption and a customized encryption scheme. We present theorems to characterize the correctness of the encryption and investigate the estimation performance and the security of the proposed method. We also analyze the impacts of the quantization errors on the estimation performance. Finally, we present numerical experiments to consolidate our analytical results.