One-to-many image encryption with privacy-preserving homomorphic outsourced decryption based on compressed sensing

As a classical framework for signal compression and reconstruction, compressed sensing (CS) has dramatically improved the state-of-the-art in many fields such as image processing, wireless communication, medical imaging and optics. Since CS integrates data compression and encryption, CS-based cryptographic primitives have attracted great attention in recent years. Note that almost all the existing CS-based encryption schemes are one-to-one symmetric cryptosystems. Unlike traditional permutation-diffusion based symmetric cryptosystems (e.g., DES and AES), CS-based encryption schemes do not have the characteristic of high efficiency because decryption procedures require to perform complex and time-consuming reconstruction algorithms. In this paper, we break the traditional one-to-one encryption framework of CS-based cryptosystems, and present an efficient one-to-many encryption model for flexible application scenarios. Specifically, the information sender first encrypts several plaintext images simultaneously for different users according to our proposed attribute-based key distribution mechanism, and then sends the ciphertext to the cloud storage server. Any user who has one of attributes can efficiently obtain the corresponding plaintext by using an outsourced decryption server (ODS). To prevent the ODS from stealing the plaintexts for illegal activities, we employ a homomorphic transformation algorithm which can transform the original reconstruction problem into a variant one. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed scheme. (C) 2019 Elsevier Inc. All rights reserved.