Secret Sharing Approach for Securing Cloud-Based Image Processing

Abstract

Cloud-based imaging, which is being increasingly used to store and process volume data/images, presents security and privacy challenges. Although these challenges have been addressed for cloud-based storage, to the best of our knowledge, they are still a concern for cloud-based volume data/image processing, such as image scaling/cropping and volume ray-casting. In this thesis, we address this concern for cloud-based image scaling/cropping and cloud-based volume ray-casting by using Shamir?s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide volume data/images in datacenters.

Firstly, we address the incompatibility issue of the floating point operations of a volume data/image processing algorithm with the modular prime operation of Shamir?s secret sharing either by converting the floating point operations to fixed point operations or by excluding the modular prime operation from secret sharing. Our analysis shows that the former technique can degrade the image quality and the latter can degrade security.

Then, we integrate secret sharing with image scaling/cropping, pre-classification volume raycasting, and post-classification volume ray-casting, and propose three cloud-based frameworks. The frameworks have been designed with the philosophy that a server secret shares volume data/image and distributes the shares (i.e., hidden data/images) among n datacenters; a datacenter, upon request from a user, processes the hidden volume data/image, and sends the processed volume data/image (which is also hidden) to the user; and the user recovers the secret processed volume data/image from k hidden processed volume data/images. Experiments and analyses show that our frameworks can provide data confidentiality, data integrity, and data availability; and can incur low computation cost to the user.