Multimedia data processing in an intelligent medical system featuring embedded elliptic curve cryptography
DOI:
https://doi.org/10.56294/hl2024.199Keywords:
Data Processing, Multimedia, Cloud computing, Edge, Blockchain, Elliptic Curve Cryptography, Intelligent Medical SystemsAbstract
Currently, global disease diagnosis is prevalent, and the evaluation of digital medical images (MI) is integral to Intelligent Medical Systems (IMS), which facilitates the early diagnosis and treatment of prevalent and severe illnesses. In this scenario, modifying or altering just one pixel of an MI during transmission across an insecure channel might result in an erroneous diagnosis, jeopardizing patient health and causing detrimental delays. Consequently, transmitting IMS multimedia information to healthcare providers presents several security issues. This study presents a Multimedia Data Processing framework using Embedded Elliptic Curve Cryptography (MDP-EECC) inside an IMS. The suggested MDP architecture comprises an Edge Level (EL), a Fog Computing (FC) stage, a Cloud computing Storage (CS) level, and a Blockchain (BC) tier. The EL gathers and transmits regular health data from the patient to the upper layer. The multimedia information from the EL is safely stored in BC-assisted CS via FC nodes using simple cryptography. Medical professionals conduct secure searches of such data for therapy or monitoring purposes. Inexpensive cryptographic methods are suggested via the use of Elliptic Curve Cryptography (ECC) with ECC-Diffie-Hellman (ECDH) and ECC-Digital Signature (ECDS) algorithms to ensure the security of MDP while preserving privacy. The suggested approach is tested using publicly accessible chest X-ray pictures. The efficacy of the suggested version is assessed and validated via comprehensive experimentation using the latest security techniques available. Compared to state-of-the-art approaches, the suggested version demonstrates superior security characteristics and can withstand different known assaults.
References
1. Mahajan, H. B., & Junnarkar, A. A. (2023). Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing. Multimedia Tools and Applications, 82(28), 44335-44358.
2. Wu, H., Dwivedi, A. D., & Srivastava, G. (2021). Security and privacy of patient information in medical systems based on blockchain technology. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(2s), 1-17.
3. Ngabo, D., Wang, D., Iwendi, C., Anajemba, J. H., Ajao, L. A., & Biamba, C. (2021). Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things. Electronics, 10(17), 2110.
4. Rathee, G., Sharma, A., Saini, H., Kumar, R., & Iqbal, R. (2020). A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology. Multimedia Tools and Applications, 79(15), 9711-9733.
5. Pal, K. (2022). A decentralized privacy preserving healthcare blockchain for iot, challenges, and solutions. In Prospects of Blockchain Technology for Accelerating Scientific Advancement in Healthcare (pp. 158-188). IGI Global.
6. Christo, M. S., Jesi, V. E., Priyadarsini, U., Anbarasu, V., Venugopal, H., & Karuppiah, M. (2021). Ensuring improved security in medical data using ecc and blockchain technology with edge devices. Security and Communication Networks, 2021(1), 6966206.
7. Aceto, G., Persico, V., & Pescapé, A. (2020). Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, 100129.
8. Abbas, A., Alroobaea, R., Krichen, M., Rubaiee, S., Vimal, S., & Almansour, F. M. (2024). Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal and ubiquitous computing, 28(1), 59-72.
9. Yeo, L. H., & Banfield, J. (2022). Human factors in electronic health records cybersecurity breach: an exploratory analysis. Perspectives in health information management, 19(Spring).
10. Reddy, K. V. S. V., & Reddy, S. N. (2021). An Improved Medical Image Watermarking Technique Based on Weber's Law Descriptors. Traitement du Signal, 38(6).
11. Rajendran, S., Krithivasan, K., & Doraipandian, M. (2021). A novel cross cosine map based medical image cryptosystem using dynamic bit-level diffusion. Multimedia Tools and Applications, 80(16), 24221-24243.
12. Kahlessenane, F., Khaldi, A., Kafi, R., & Euschi, S. (2021). A robust blind medical image watermarking approach for telemedicine applications. Cluster computing, 24(3), 2069-2082.
13. Devi, H. S., & Mohapatra, H. (2023). A novel robust blind medical image watermarking using rank-based DWT. International Journal of Information Technology, 15(4), 1901-1909.
14. Alhomoud, A. M. (2021). Image steganography in spatial domain: Current status, techniques, and trends. Intelligent Automation & Soft Computing, 27(1).
15. Zhang, X., & Wang, X. (2019). Multiple-image encryption algorithm based on DNA encoding and chaotic system. Multimedia Tools and Applications, 78(6), 7841-7869.
16. Chen, J., Zhu, Z. L., Zhang, L. B., Zhang, Y., & Yang, B. Q. (2018). Exploiting self-adaptive permutation–diffusion and DNA random encoding for secure and efficient image encryption. Signal Processing, 142, 340-353.
17. Belazi, A., Talha, M., Kharbech, S., & Xiang, W. (2019). Novel medical image encryption scheme based on chaos and DNA encoding. IEEE access, 7, 36667-36681.
18. Zefreh, E. Z. (2020). An image encryption scheme based on a hybrid model of DNA computing, chaotic systems and hash functions. Multimedia Tools and Applications, 79(33), 24993-25022.
19. Benssalah, M., Rhaskali, Y., & Drouiche, K. (2021). An efficient image encryption scheme for TMIS based on elliptic curve integrated encryption and linear cryptography. Multimedia Tools and Applications, 80(2), 2081-2107.
20. Hayat, U., Ullah, I., Azam, N. A., & Azhar, S. (2022). A novel image encryption scheme based on elliptic curves over finite rings. Entropy, 24(5), 571.
21. Farwa, S., Bibi, N., & Muhammad, N. (2020). An efficient image encryption scheme using Fresnelet transform and elliptic curve based scrambling. Multimedia Tools and Applications, 79, 28225-28238.
22. https://www.kaggle.com/tawsifurrahman/covid 19-radiography-database
23. Chukwusa, J. (2021). An Assessment of the Information Literacy Skills of Students in Nigerian Universities. Indian Journal of Information Sources and Services, 11(1), 9–15. https://doi.org/10.51983/ijiss-2021.11.1.2649
24. Rakesh, N., Mohan, B. A., Kumaran, U., Prakash, G. L., Arul, R., & Thirugnanasambandam, K. (2024). Machine learning-driven strategies for customer retention and financial improvement. Archives for Technical Sciences, 2(31), 269–283. https://doi.org/10.70102/afts.2024.1631.269
25. Ilori Maria, E., Shutti Bolaji, S., Oyintola Isiaka, A., Abdullahi Mostura, A., & Oluwafemi Segun, V. (2021). Evaluation of Public Services by its Users of an Academic Library, Lagos State University (LASU), Ojo, Nigeria. Indian Journal of Information Sources and Services, 11(1), 41–46. https://doi.org/10.51983/ijiss-2021.11.1.2653
26. Biswas, B., Neogi, S., & Roy, B. (2024). Application of delighting to optimize window-to-wall ratio (wwr) in buildings in indian climatic conditions. Archives for Technical Sciences, 2(31), 248–258. https://doi.org/10.70102/afts.2024.1631.248
27. Muthuraja, S., Lakshmisha, H., Nagaraja, H., & Arunkumar, M. P. (2021). Webometric Analysis of Selected Universities Websites in Karnataka: An Evaluative Study Using Alexa Internet. Indian Journal of Information Sources and Services, 11(1), 22–27. https://doi.org/10.51983/ijiss-2021.11.1.2810
28. Nandhinieswari, S., & Indumathi, A. (2024). Bilevel optimized recursive feature eliminator for cervical cancer feature selection process. Archives for Technical Sciences, 2(31), 311–328. https://doi.org/10.70102/afts.2024.1631.311
29. Ilori, M. E., Oluwafemi, V. S., & Odusina, E. S. (2020). School Library Services as a Catalyst for the Better Basic Education in Nigeria. Indian Journal of Information Sources and Services, 10(1), 1–6. https://doi.org/10.51983/ijiss.2020.10.1.485
30. Suljić, N., & Kovčić, O. (2018). Analysis of Time Oscillations of Water on Lake Modric as a Multi-Purpose Reservoir. Archives for Technical Sciences, 1(18), 31–40.
31. Saidova, K., & et al. (2024). Developing framework for role of mobile app in promoting aquatic education and conservation awareness among general people. International Journal of Research and Environmental Studies. 4. 58-63. 10.70102/IJARES/V4S1/10.
32. Saidova, K., & et al. (2024). Assessing the Economic Benefits of Climate Change Mitigation and Adoption Strategies for Aquatic Ecosystem. International Journal of Research and Environmental Studies. 4. 20-26. 10.70102/IJARES/V4S1/4.
33. Saidova, K., & et al. (2024). Assessing the impact of invasive species on native aquatic ecosystems and developing management strategies. International Journal of Research and Environmental Studies. 4. 45-51. 10.70102/IJARES/V4S1/8.
34. Deepthi, K. J., Balakrishnan, T. S., Krishnan, P., & Ebenezar, U. S. (2024, June). Optimized Data Storage Algorithm of IoT Based on Cloud Computing in Distributed System. In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 (pp. 1-5). IEEE.
35. Sasikala, R., Deepthi, K. J., Balakrishnan, T. S., Krishnan, P., & Ebenezar, U. S. (2024, June). Machine Learning-Enhanced Analysis of Genomic Data for Precision Medicine. In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 (pp. 1-5). IEEE.
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Copyright (c) 2024 Muslima Abdullaeva, Nafisa Turaeva , Shaxodat Yadgarova , Dilrabo Khalimova, Elnora Eshonkulova , Madina Yormatova , Qiyom Nazarov (Author)
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