Controllable artificial watermark injected parallel compressive sensing for simultaneous compression-encryption applications

Abstract

The emerging compressed sensing (CS) enables compression and encryption simultaneously, which is very suitable for the resource-constraint Internet of things (IoT) applications. However, traditional CS-based cryptosystem can not provide efficient resistance to known-plaintext attack (KPA) under the multi-time-sampling (MTS) scenario. A novel CS-based privacy-preserving cryptosystem, called PCS-CAW (parallel CS injected with controllable artificial watermark), for simultaneous compression-encryption applications is proposed. Firstly, the original plaintext is scrambled by the global random permutation (GRP) operation. Then, the novel watermark injected parallel CS (PCS) is developed to re-encrypt and compress the intermediate ciphertext. Since a controllable artificial random watermark is injected into PCS sampling processing, the proposed PCS-CAW cryptosystem provides efficient resistance to KPA under the MTS scenario. In the decoding stage, a distinctive watermark removing strategy is developed. Experiments demonstrate that the proposed cryptosystem can achieve superior security and compression performance than previous CS-based ones.