Journal of Cybersecurity and Information Management

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https://doi.org/10.54216/JCIM

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2690-6775ISSN (Online) 2769-7851ISSN (Print)

Volume 7 , Issue 2 , PP: 77-84, 2021 | Cite this article as | XML | Html | PDF | Full Length Article

An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security

Mustafa s Khalifa 1 * , Ahmed N. Al-Masri 2

  • 1 Computer Science Department, Faculty of Education Pure Science, Basra University, Iraq - (Mustafa.khalefa@uobasrah.edu.iq )
  • 2 American University in the Emirates, Dubai, UAE - (ahmed.almasri@aue.ae)
  • Doi: https://doi.org/10.54216/JCIM.070203

    Received: April 15, 2021 Accepted: August 02, 2021
    Abstract

    Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.

    Keywords :

    Image encryption, Security, Key generation, Homomorphic encryption, TLBO algorithm, Optimization process  ,

    References

    [1]      Noshadian, S., Ebrahimzade, A. and Kazemitabar, S.J., 2018. Optimizing chaos based image encryption. Multimedia Tools and Applications, 77(19), pp.25569-25590.

    [2]      Li, G.D., 2019. Double chaotic image encryption algorithm based on optimal sequence solution and fractional transform. The Visual Computer, 35(9), pp.1267-1277.

    [3]      Abdullah, A.H., Enayatifar, R. and Lee, M., 2012. A hybrid genetic algorithm and chaotic function model for image encryption. AEU-International Journal of Electronics and Communications, 66(10), pp.806-816.

    [4]      Enayatifar, R., Abdullah, A.H. and Isnin, I.F., 2014. Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence. Optics and Lasers in Engineering, 56, pp.83-93.

    [5]      An, F.P. and Liu, J.E., 2019. Image encryption algorithm based on adaptive wavelet chaos. Journal of Sensors, 2019.

    [6]      Shankar, K. and Eswaran, P., 2016. An efficient image encryption technique based on optimized key generation in ECC using genetic algorithm. In Artificial intelligence and evolutionary computations in engineering systems (pp. 705-714). Springer, New Delhi.

    [7]      Soni, A. and Agrawal, S., 2012. Using genetic algorithm for symmetric key generation in image encryption. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), pp.137-140.

    [8]      Niu, Y., Zhou, Z. and Zhang, X., 2020. An image encryption approach based on chaotic maps and genetic operations. Multimedia Tools and Applications, 79(35), pp.25613-25633.

    [9]      Noshadian, S., Ebrahimzade, A. and Kazemitabar, S.J., 2020. Breaking a chaotic image encryption algorithm. Multimedia Tools and Applications, 79(35), pp.25635-25655.

    [10]   Pashakolaee, P.G., Shahhoseini, H.S. and Mollajafari, M., 2018. Hyper-chaotic Feeded GA (HFGA): a reversible optimization technique for robust and sensitive image encryption. Multimedia Tools and Applications, 77(16), pp.20385-20414.

    [11]   Kaur, M. and Kumar, V., 2018. Adaptive differential evolution-based lorenz chaotic system for image encryption. Arabian Journal for Science and Engineering, 43(12), pp.8127-8144.

    [12]   Enayatifar, R., Abdullah, A.H. and Lee, M., 2013. A weighted discrete imperialist competitive algorithm (WDICA) combined with chaotic map for image encryption. Optics and Lasers in Engineering, 51(9), pp.1066-1077.

    [13]   Khan, L.S., Hazzazi, M.M., Khan, M. and Jamal, S.S., 2021. A novel image encryption based on rossler map diffusion and particle swarm optimization generated highly non-linear substitution boxes. Chinese Journal of Physics.

    [14]   Zeng, J. and Wang, C., 2021. A novel hyperchaotic image encryption system based on particle swarm optimization algorithm and cellular automata. Security and Communication Networks, 2021. 

    [15]   Farah, M.A., Farah, A. and Farah, T., 2020. An image encryption scheme based on a new hybrid chaotic map and optimized substitution box. Nonlinear Dynamics, 99(4), pp.3041-3064. 

    [16]   Wang, X. and Li, Y., 2021. Chaotic image encryption algorithm based on hybrid multi-objective particle swarm optimization and DNA sequence. Optics and Lasers in Engineering, 137, p.106393. 

    [17]   Kaur, M. and Singh, D., 2021. Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption. Multidimensional Systems and Signal Processing, 32(1), pp.281-301.

    [18]   Yin, S. and Li, H., 2020. GSAPSO-MQC: medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system. Evolutionary Intelligence, pp.1-13.

    [19]   Ghazvini, M., Mirzadi, M. and Parvar, N., 2020. A modified method for image encryption based on chaotic map and genetic algorithm. Multimedia Tools and Applications, 79(37), pp.26927-26950.

    [20]   Shankar, K. and Lakshmanaprabu, S.K., 2018. Optimal key based homomorphic encryption for color image security aid of ant lion optimization algorithm. International Journal of Engineering & Technology, 7(9), pp.22-27.

    [21]    Shankar, K., Lakshmanaprabu, S.K., Gupta, D., Khanna, A. and de Albuquerque, V.H.C., 2020. Adaptive optimal multi key based encryption for digital image security. Concurrency and Computation: Practice and Experience, 32(4), p.e5122.

    [22]   Singh, M., Panigrahi, B.K. and Abhyankar, A.R., 2013. Optimal coordination of directional over-current relays using Teaching Learning-Based Optimization (TLBO) algorithm. International Journal of Electrical Power & Energy Systems, 50, pp.33-41.

    Cite This Article As :
    s, Mustafa. , N., Ahmed. An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security. Journal of Cybersecurity and Information Management, vol. , no. , 2021, pp. 77-84. DOI: https://doi.org/10.54216/JCIM.070203
    s, M. N., A. (2021). An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security. Journal of Cybersecurity and Information Management, (), 77-84. DOI: https://doi.org/10.54216/JCIM.070203
    s, Mustafa. N., Ahmed. An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security. Journal of Cybersecurity and Information Management , no. (2021): 77-84. DOI: https://doi.org/10.54216/JCIM.070203
    s, M. , N., A. (2021) . An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security. Journal of Cybersecurity and Information Management , () , 77-84 . DOI: https://doi.org/10.54216/JCIM.070203
    s M. , N. A. [2021]. An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security. Journal of Cybersecurity and Information Management. (): 77-84. DOI: https://doi.org/10.54216/JCIM.070203
    s, M. N., A. "An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security," Journal of Cybersecurity and Information Management, vol. , no. , pp. 77-84, 2021. DOI: https://doi.org/10.54216/JCIM.070203