Neural Network Model for Recommending Music Based on Music Genres

Abstract

Present era is marked by exponential growth in transfer of multimedia data through internet. Most of the Internet-of-Things(IoT) applications send images to cloud storages through internet. However, in sensitive applications such as healthcare, defense, etc., these images should be encrypted before transmission through insecure public channels to gateway fog nodes. Conventional encryption algorithms cannot be used there due to the resource constraint characters of IoT devices. Here, Cellular Automata (CA) based encryption algorithms can be used because of their inherent simplicity in implementation in hardware, without affecting the capability of generating highly random sequences. In this paper, a lightweight, robust and secure image encryption technique has been proposed using 2-D Von-Neumann Cellular Automata (VCA), called IEVCA, which is lossless, correlation immune and has all the essential properties of a good image cipher. Additionally, the proposed technique passes all the randomness tests of DIEHARD and NIST statistical test suites. Moreover, several security and performance analyses of the IEVCA proved its efficiency and resistance against security attacks. Experimental results of the IEVCA show its better performance when compared to the existing encryption techniques.