Harnessing the Hybrid Cloud for Secure Big Image Data Service

Abstract

Various kinds of image sensors capture a large number of images in Internet of Things (IoT) every day. It is increasingly concerned how to securely store and share these big image data from IoT. In this paper, we harness the hybrid cloud to provide secure big image data storage and share service for users. The basic idea is to partition each image into a small set of sensitive data and a large set of insensitive data, which are securely stored in the private cloud and the public cloud, respectively. Specially, the private cloud divides each image into the sensitive data (<20%) and the insensitive data (>80%) based on sensitivity identification approaches like Sobel edge detector. The sensitive data are encrypted in parallel at a counter mode and then stored in the private cloud. The insensitive data are encrypted-then-subsampled and then placed in the public cloud, in which the encryption employs the permutation-diffusion architecture and the subsampling utilizes compressed sampling technique. The keystreams used in encryption operations are managed by the tent-logistic system with high initial value sensitivity. Once users make a request for an image, the public cloud provides a privacy-guaranteed insensitive data reconstruction service, and the private cloud decrypts the sensitive and insensitive data and regroups them into a complete image. Experimental results demonstrate that the proposed framework can provide secure big image data service.