Low-Complexity Cloud Image Privacy Protection via Matrix Perturbation
  Cloud-assisted image services are widely used for various applications. Due to the high computational complexity of existing image encryption technology, it is extremely challenging to provide privacy preserving image services for resource-constrained smart device. In this paper, we propose a novel encrypressive cloud-assisted image service scheme, called eCIS. The key idea of eCIS is to shift the high computational cost to the cloud allowing reduction in complexity of encoder and decoder on resource-constrained device. This is done via compressive sensing (CS) techniques, compared with existing approaches, we are able to achieve privacy protection at no additional transmission cost. In particular, we design an encryption matrix by taking care of image compression and encryption simultaneously. Such that, the goal of our design is to minimize the mutual information of original image and encrypted image. In addition to the theoretical analysis that demonstrates the security properties and complexity of our system, we also conduct extensive experiment to evaluate its performance. The experiment results show that eCIS can effectively protect image privacy and meet the user's adaptive secure demand. eCIS reduced the system overheads by up to $4.1\times\sim6.8\times$ compared with the existing CS based image processing approach. 
Xuangou Wu, Shaojie Tang, Panlong Yang
cs.CR cs.NI