Distributed privacy-preserving nested compressed sensing for multiclass data collection with identity authentication

Abstract

With the increase of new sensing devices in Internet of things (IoT), data dimensions and types have risen dramatically. The traditional data collection structure cannot satisfy the requirements for multiclass data and access control. By integrating fog computing, we design a layer-aware fog computing model which supports a distributed privacy-preserving compressed sensing (CS) for multiclass data with identity authentication in fog-assisted IoT. In our model, a novel distributed nested CS scheme samples and compresses the encrypted multiclass data in the sensing layer. The encrypted sampling data is transmitted to the fog node. Subsequently, the fog node embeds the identity watermark for later VIP user authentication in the cloud. Then, the fog uploads the watermarked data to the cloud for storage and reconstruction. When receiving the request, all results returned by the cloud are encrypted versions. In particular, the cloud performs the designed reconstruction transformation task on the sensing data for security. Specifically, the cloud only reconstructs sensing data for general users, but returns all multiclass data to the authenticated VIP users. In the experiment, we analyze the security of our scheme, discuss the impact of parameters on the reconstruction performance of different data, and summarize the recommended parameter settings.