TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Privacy-preserving medical IoT using cloud-based medical imaging archives

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pp. 3681–3692Vol. 47Issue 10October 2026DOI: 10.47974/JIOS-2494XML
Received:
01 Mar 2026
Published Online:
31 Oct 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2494
Pages:
3681–3692

Abstract

The Internet of Medical Things (IoMT) has the potential to enhance the storage, sharing and remote access of medical images and the introduction of IoMT into the cloud-based medical imaging archive raises many privacy and security concerns. This research introduces a novel approach to protect medical images across their entire lifecycle, known as Medical IoT, that ensures privacy protection of medical images. The proposed approach combines patient identity protection, separation of metadata, medical-image encryption, secure key management, integrity checking, authenticated medical-image transmission, controlled cloud storage, and authorized reconstruction of medical images. The architecture enables the secure communication between, but not limited to, medical imaging devices, IoMT gateways, cloud archives and healthcare providers. It is tested with the help of CT, MRI, X-ray and ultrasound images based on computational and security metrics such as encryption and decryption time, utilization of resources, entropy, pixel correlation, NPCR, UACI, key sensitivity, and the quality of reconstruction. The proposed method not only ensures privacy and security of medical images but also enhances computational efficiency, offering a scalable solution for handling medical images securely in resource-limited, cloud-based healthcare settings.

Keywords

Subject Classifications

68R05

References

[1] S. Madanian, T. Chinbat, M. Subasinghage, D. Airehrour, F. Hassandoust, and S. Yongchareon, “Health IoT threats: Survey of risks and vulnerabilities,” Future Internet, vol. 16, Art. no. 389 (2024).

[2] M. Zhang, Y. Chen, and W. Susilo, “PPO-CPQ: A privacy-preserving optimization of clinical pathway query for e-healthcare systems,” IEEE Internet Things J., vol. 7, pp. 10660–10672 (2020).

[3] T. E. Ali, F. I. Ali, P. Dakić, and A. D. Zoltan, “Trends, prospects, challenges, and security in the healthcare Internet of Things,” Computing, vol. 107, Art. no. 28 (2025).

[4] V. B. Charles, D. Surendran, and A. SureshKumar, “Heart disease data based privacy preservation using enhanced ElGamal and ResNet classifier,” Biomed. Signal Process. Control, vol. 71, Art. no. 103185 (2022).

[5] A. Pradeepa and P. Arathi, “Degree-based topological indices of rhombus type silicate and oxide networks using M-polynomial,” J. Inf. Optim. Sci., vol. 47, no. 2, pp. 553–568 (2025), doi: 10.47974/JIOS-1809.

[6] C. L. Stergiou, K. E. Psannis, and B. B. Gupta, “InFeMo: Flexible big data management through a federated cloud system,” ACM Trans. Internet Technol., vol. 22, Art. no. 46 (2022).

[7] N. M. Hijazi, M. Aloqaily, M. Guizani, B. Ouni, and F. Karray, “Secure federated learning with fully homomorphic encryption for IoT communications,” IEEE Internet Things J., vol. 11, pp. 4289–4300 (2023).

[8] S. Selvaraj and S. Sundaravaradhan, “Challenges and opportunities in IoT healthcare systems: A systematic review,” SN Appl. Sci., vol. 2, Art. no. 139 (2020).

[9] A. Aminifar, M. Shokri, F. Rabbi, V. K. I. Pun, and Y. Lamo, “Extremely randomized trees with privacy preservation for distributed structured health data,” IEEE Access, vol. 10, pp. 6010–6027 (2022).

[10] N. Byabarta and S. K. Mitra, “Adaptive sensor linearization: A hybrid approach utilizing polynomial, spline, RBF and deep neural network methods,” J. Stat. Manag. Syst., vol. 29, no. 1, pp. 101–110 (2025), doi: 10.47974/JSMS-1526.

[11] A. D. Shewale and S. V. Sankpal, “IoT & Raspberry Pi based smart and secure health care system using BSN,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 2, pp. 506–510 (2020), doi: 10.22214/ijraset.2020.2077.

[12] A. Morchid, M. Marhoun, R. El Alami, and B. Boukili, “Intelligent detection for sustainable agriculture: A review of IoT-based embedded systems, cloud platforms, DL, and ML for plant disease detection,” Multimed. Tools Appl., vol. 83, no. 28, pp. 70961–71000 (2024), doi: 10.1007/s11042-024-18392-9.

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