Privacy-preserving image compressed sensing by embedding a controllable noise-injected transformation for IoT devices

Abstract

Recently, compressed sensing (CS) based cryptosystem has received extensive attention in information security field. However, this cryptosystem cannot resist known-plaintext attack (KPA) under the secret key multi-time-using (MTU) scenario because of its linear sampling process. To address this concern, a privacy-preserving image CS scheme is proposed, which embeds a controllable noise-injected transformation (CNT) into the linear sampling process of CS to resist KPA. First, a CNT operation is applied to pre-encrypt the image. Second, the pre-encrypted image is re-encrypted and compressed by using CS at the same time. Third, the final CS measurements are quantized into bits. Since the embedding of CNT operation destroys the linearity of CS, the proposed cryptosystem can resist the existing KPA method under the secret key MTU scenario. Lastly, an iterative total variation (TV) algorithm based on ADMM framework, called TV-ADMM, is proposed for image recovery. Simulation results demonstrate that the proposed cryptosystem significantly enhances the security of the CS-based cryptosystem with slightly sacrificing the compression performance in high compression rate cases.