A resource-efficient encryption algorithm for multimedia big data

Nowadays, multimedia is considered to be the biggest big data as it dominates the traffic in the Internet and mobile phones. Currently symmetric encryption algorithms are used in IoT but when considering multimedia big data in IoT, symmetric encryption algorithms incur more computational cost. In this paper, we have designed and developed a resource-efficient encryption system for encrypting multimedia big data in IoT. The proposed system takes the advantages of the Feistel Encryption Scheme, an Advanced Encryption Standard (AES), and genetic algorithms. To satisfy high throughput, the GPU has also been used in the proposed system. This system is evaluated on real IoT medical multimedia data to benchmark the encryption algorithms such as MARS, RC6, 3-DES, DES, and Blowfish in terms of computational running time and throughput for both encryption and decryption processes as well as the avalanche effect. The results show that the proposed system has the lowest running time and highest throughput for both encryption and decryption processes and highest avalanche effect with compared to the existing encryption algorithms. To satisfy the security objective, the developed algorithm has better Avalanche Effect with compared to any of the other existing algorithms and hence can be incorporated in the process of encryption/decryption of any plain multimedia big data. Also, it has shown that the classical and modern ciphers have very less Avalanche Effect and hence cannot be used for encryption of confidential multimedia messages or confidential big data. The developed encryption algorithm has higher Avalanche Effect and for instance, AES in the proposed system has an Avalanche Effect of %52.50. Therefore, such system is able to secure the multimedia big data against real-time attacks.