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International Journal of Science, Strategic Management and Technology

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A SECURE FEDERATED HEALTHCARE ANALYTICS SYSTEM USING ECC FOR PRIVACY-PRESERVING DISEASE DIAGNOSIS

AUTHORS:
Logesh A
Ashik Ahamed A
Mohammed Biyas R
Aslam AN
Mentor
Affiliation
Department of Computer Science and Engineering,MIET Engineering College, Tiruchirappalli – 620007
DOI: 10.55041/ijsmt.v2i4.191
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

This paper presents a secure federated healthcare analytics system for privacy-preserving disease diagnosis using Elliptic Curve Cryptography (ECC). Federated learning enables multiple healthcare institutions to collaboratively train diagnostic models without sharing sensitive patient data. ECC is applied to encrypt model updates, ensuring secure communication and strong data protection with low computational overhead. A Multi-Layer Perceptron (MLP) model is utilized for accurate disease prediction using distributed medical datasets. Blockchain technology records all encrypted updates and access activities in an immutable ledger, ensuring transparency and trust among participants. The proposed system enhances data privacy, security, and diagnostic accuracy, making it suitable for modern healthcare applications

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 10 2026
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A, L., A, A. A., R, M. B. & AN, A. (2026). A Secure Federated Healthcare Analytics System Using ECC For Privacy-Preserving Disease Diagnosis. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.191

A, Logesh, et al.. "A Secure Federated Healthcare Analytics System Using ECC For Privacy-Preserving Disease Diagnosis." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.191.

A, Logesh,Ashik A,Mohammed R, and Aslam AN. "A Secure Federated Healthcare Analytics System Using ECC For Privacy-Preserving Disease Diagnosis." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.191.

References
1.Shahid et al., "Data protection and privacy of the internet of healthcare things (IoHTs)," Applied Sciences, vol. 12, no. 4, p. 1927,2022.

2.Huang, "Ethics of artificial intelligence in education: Student privacy and data protection," Science Insights Education Frontiers, vol. 16, no. 2, pp. 2577–2587,2023.

3.S. Bakare et al., "Data privacy laws and compliance: a comparative review of the EU GDPR and USA regulations," 2024.

4.Li et al., "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, vol. 329, p. 120291, 2023.

5.Villegas-Ch and J. García-Ortiz, "Toward a comprehensive framework for ensuring security and privacy in artificial intelligence," Electronics, vol. 12, no. 18, p. 3786, 2023.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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