Biblio

In the era of Cloud and Social Networks, mobile devices exhibit much more powerful abilities for big media data storage and sharing. However, many users are still reluctant to share/store their data via clouds due to the potential leakage of confidential or private information. Although some cloud services provide storage encryption and access protection, privacy risks are still high since the protection is not always adequately conducted from end-to-end. Most customers are aware of the danger of letting data control out of their hands, e.g., Storing them to YouTube, Flickr, Facebook, Google+. Because of substantial practical and business needs, existing cloud services are restricted to the desired formats, e.g., Video and photo, without allowing arbitrary encrypted data. In this paper, we propose a format-compliant end-to-end privacy-preserving scheme for media sharing/storage issues with considerations for big data, clouds, and mobility. To realize efficient encryption for big media data, we jointly achieve format-compliant, compression-independent and correlation-preserving via multi-channel chained solutions under the guideline of Markov cipher. The encryption and decryption process is integrated into an image/video filter via GPU Shader for display-to-display full encryption. The proposed scheme makes big media data sharing/storage safer and easier in the clouds.

Security is becoming a major concern in computing. New techniques are evolving every day; one of these techniques is Hash Visualization. Hash Visualization uses complex random generated images for security, these images can be used to hide data (watermarking). This proposed new technique improves hash visualization by using genetic algorithms. Genetic algorithms are a search optimization technique that is based on the evolution of living creatures. The proposed technique uses genetic algorithms to improve hash visualization. The used genetic algorithm was away faster than traditional previous ones, and it improved hash visualization by evolving the tree that was used to generate the images, in order to obtain a better and larger tree that will generate images with higher security. The security was satisfied by calculating the fitness value for each chromosome based on a specifically designed algorithm.