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Neural Attractor-Based Adaptive Key Generator with DNA-Coded Security and Privacy Framework for Multimedia Data in Cloud Environments

Author

Listed:
  • Hemalatha Mahalingam

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 22254, Saudi Arabia)

  • Padmapriya Velupillai Meikandan

    (Department of Computer Sciences, Marquette University, Milwaukee, WI 53233, USA)

  • Karuppuswamy Thenmozhi

    (School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)

  • Kawthar Mostafa Moria

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 22254, Saudi Arabia)

  • Chandrasekaran Lakshmi

    (School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)

  • Nithya Chidambaram

    (School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)

  • Rengarajan Amirtharajan

    (School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)

Abstract

Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented preemptively through a host of proven cryptographical solutions. The presented work integrates adaptive key generation, neural-based confusion and non-XOR, namely DNA diffusion, which offers a more extensive and unique key, adaptive confusion and unpredictable diffusion algorithm. Only authenticated users can store this encrypted image in cloud storage. The proposed security framework uses logistics, tent maps and adaptive key generation modules. The adaptive key is generated using a multilayer and nonlinear neural network from every input plain image. The Hopfield neural network (HNN) is a recurrent temporal network that updates learning with every plain image. We have taken Amazon Web Services (AWS) and Simple Storage Service (S3) to store encrypted images. Using benchmark evolution metrics, the ability of image encryption is validated against brute force and statistical attacks, and encryption quality analysis is also made. Thus, it is proved that the proposed scheme is well suited for hosting cloud storage for secure images.

Suggested Citation

  • Hemalatha Mahalingam & Padmapriya Velupillai Meikandan & Karuppuswamy Thenmozhi & Kawthar Mostafa Moria & Chandrasekaran Lakshmi & Nithya Chidambaram & Rengarajan Amirtharajan, 2023. "Neural Attractor-Based Adaptive Key Generator with DNA-Coded Security and Privacy Framework for Multimedia Data in Cloud Environments," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1769-:d:1118299
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    References listed on IDEAS

    as
    1. Sungwook Eom & Jun-Ho Huh, 2018. "The Opening Capability for Security against Privacy Infringements in the Smart Grid Environment," Mathematics, MDPI, vol. 6(10), pages 1-14, October.
    2. Shaista Mansoor & Parsa Sarosh & Shabir A. Parah & Habib Ullah & Mohammad Hijji & Khan Muhammad, 2022. "Adaptive Color Image Encryption Scheme Based on Multiple Distinct Chaotic Maps and DNA Computing," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    3. Shenli Zhu & Xiaoheng Deng & Wendong Zhang & Congxu Zhu, 2023. "Image Encryption Scheme Based on Newly Designed Chaotic Map and Parallel DNA Coding," Mathematics, MDPI, vol. 11(1), pages 1-22, January.
    4. Huiyan Zhong & Guodong Li & Xiangliang Xu & Xiaoming Song, 2022. "Image Encryption Algorithm Based on a Novel Wide-Range Discrete Hyperchaotic Map," Mathematics, MDPI, vol. 10(15), pages 1-23, July.
    5. Xianhua Song & Guanglong Chen & Ahmed A. Abd El-Latif, 2022. "Quantum Color Image Encryption Scheme Based on Geometric Transformation and Intensity Channel Diffusion," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
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