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A multithreaded programming approach for multimedia big data: encryption system

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Abstract

Multimedia is currently seen to dominate the internet network and the mobile network traffic; hence, it is seen as the largest Big data. Generally, the symmetric encryption algorithms are applied to the ‘big multimedia data’; however; these algorithms are thought as very slow. In our study, we developed and designed a resource-efficient encryption algorithm system which applies the multithreaded programming process for the encryption of the big multimedia data. This proposed system describes a multi-level encryption model which uses the Feistel Encryption Scheme, genetic algorithms and the Advanced Encryption Standard (AES). Our system has been assessed for actual medical-based big multimedia data and compared to the benchmarked encryption algorithms like the RC6, MARS, 3-DES, DES, and Blowfish with regards to the computational run time and its throughput for the encryption and decryption procedures. In addition, the multithreaded programming approach is adopted to implement the proposed encryption system in order to enhace the system effeciencey and porfermance. Furthermore, we also compared our system with its sequential version for showing its resource efficiency. Our results indicated that our system had the least run time and a higher throughput for the encryption and decryption processes in comparison to the already existing standard encryption algorithms. Also, our system could improve the computation run time by approximately 75% and its throughput was also increased by 4-times in comparison to its sequential version. For fulfilling the security objectives, our algorithm showed a better Avalanche Effect in comparison to the existing algorithms and therefore, could be included in any encryption/decryption process of a big plain multimedia data.

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Correspondence to Shadi Aljawarneh.

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Aljawarneh, S., Yassein, M.B. & Talafha, W.A. A multithreaded programming approach for multimedia big data: encryption system. Multimed Tools Appl 77, 10997–11016 (2018). https://doi.org/10.1007/s11042-017-4873-9

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  • DOI: https://doi.org/10.1007/s11042-017-4873-9

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