Abstract
A very high degree of security is required for the transmission of medical pictures via open access as these images are more important than other types of images in most applications, especially real-time applications like telemedicine. The rapid advancement of artificial intelligence (AI) technology has made the privacy and security of patient medical picture data an urgent issue in the field of image privacy protection. By combining quantum deep learning with cyber security research of cloud IoT networks, this study proposes a novel approach to encrypting medical photos. Here, a stream crypto cypher and deep, extreme convolutional networks encrypt the medical image. Afterwards, this encrypted picture was stored using a secure cloud IoT infrastructure. Encryption speed, structural similarity index measure (SSIM), root mean square error (RMSE), mean average precision (MAP), and peak signal-to-noise ratio (PSNR) are all used in the experimental investigation. To determine the efficacy of the proposed approach, experimental analyses and simulations were carried out. PSNR was 92%, RMSE was 85%, SSIM was 68%, MAP was 52%, and encryption speed was 88% using the suggested method.
Similar content being viewed by others
Data availability
The data used in this study are available upon request.
References
Arumugam, S., Annadurai, K.: An efficient machine learning based image encryption scheme for medical image security. J. Med. Imaging Health Inf. 11(6), 1533–1540 (2021)
Dimililer, K.: DCT-based medical image compression using machine learning. SIViP 16(1), 55–62 (2022)
Ding, Y., Wu, G., Chen, D., Zhang, N., Gong, L., Cao, M., Qin, Z.: DeepEDN: A deep-learning-based image encryption and decryption network for internet of medical things. IEEE Internet Things J. 8(3), 1504–1518 (2020)
Ding, Y., Tan, F., Qin, Z., Cao, M., Choo, K.K.R., Qin, Z.: DeepKeyGen: a deep learning-based stream cipher generator for medical image encryption and decryption. IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4915–4929 (2021)
Faes, L., Wagner, S.K., Fu, D.J., Liu, X., Korot, E., Ledsam, J.R., Keane, P.A.: Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Dig. Health 1(5), e232–e242 (2019)
Gadde, S., Amutharaj, J., Usha, S.: A security model to protect the isolation of medical data in the cloud using hybrid cryptography. J. Inf. Secur. Appl. 73, 103412 (2023)
Huang, Q.X., Yap, W.L., Chiu, M.Y., Sun, H.M.: Privacy-preserving deep learning with learnable image encryption on medical images. IEEE Access 10, 66345–66355 (2022)
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Braren, R.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473–484 (2021)
Lata, K., Cenkeramaddi, L.R.: Deep learning for medical image cryptography: a comprehensive review. Appl. Sci. 13(14), 8295 (2023)
Ma, Y., Chai, X., Gan, Z., & Zhang, Y. Privacy-Preserving TPE-based JPEG image retrieval in cloud-assisted internet of things. IEEE Internet Things J. (2023).
Qamar, S.: Federated convolutional model with cyber blockchain in medical image encryption using Multiple Rossler lightweight Logistic sine mapping. Comput. Electr. Eng. 110, 108883 (2023)
Rajesh Kumar, N., Bala Krishnan, R., Manikandan, G., Subramaniyaswamy, V., Kotecha, K.: Reversible data hiding scheme using deep learning and visual cryptography for medical image communication. J. Electron. Imaging 31(6), 063028–063028 (2022)
Rathod, S., Salunke, M.D., Yashwante, M., Bhende, M., Rangari, S.R., Rewaskar, V.D.: Ensuring optimized storage with data confidentiality and privacy-preserving for secure data sharing model over cloud. Int. J. Intell. Syst. Appl. Eng. 11(3), 35–44 (2023)
Sujatha, G., Devipriya, A., Brindha, D., & Premalatha, G.: An efficient cloud storage model for GOP-level video deduplication using adaptive GOP structure. Cybern. Syst. 1–26 (2023).
Tyagi, S.S.: Enhancing security of cloud data through encryption with AES and Fernet algorithm through convolutional-neural-networks (CNN). Int. J. Comput. Netw. Appl. 8(4), 288–299 (2021)
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
The Single author contributed significantly to the study and preparation of this manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest that could influence the research or the interpretation of results presented in this manuscript.
Ethical approval
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, J. Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model. Opt Quant Electron 56, 432 (2024). https://doi.org/10.1007/s11082-023-06076-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-023-06076-x