Privacy-preserving kidney image analysis via blockchain-secured federated deep learning framework

Mahalakshmi A; Dhavamani V

Privacy-preserving kidney image analysis via blockchain-secured federated deep learning framework

Author(s)	Ms. Mahalakshmi A, Prof. Dhavamani V
Country	India
Abstract	The healthcare industry faces challenges in diagnosing kidney diseases while protecting patient data privacy. Large amounts of sensitive medical data are generated daily through medical imaging and electronic health records. Traditional centralized systems require storing all data in a single server, which raises privacy and security concerns. To solve this issue, the proposed system uses Federated Learning (FL) to allow multiple hospitals to train models collaboratively without sharing raw patient data. The system supports different input types such as medical images and structured datasets. For image-based data, a Convolutional Neural Network (CNN) is used for accurate disease classification. For structured patient data, a Multi-Layer Perceptron (MLP) model performs analysis and prediction. After local training, each model is encrypted using Elliptic Curve Cryptography (ECC) to ensure secure transmission. The encrypted models are then uploaded to a Blockchain-based server that records updates in a transparent and tamper-proof ledger. This integrated approach improves privacy, security, and accuracy in kidney disease diagnosis. This approach supports early detection and improves healthcare decision-making. This hybrid approach improves diagnostic reliability across diverse medical data types while maintaining strict privacy protection. Ultimately, the framework supports early disease detection, improves clinical decision-making, and contributes to better patient care in the modern healthcare ecosystem.
Keywords	Federated Learning, Blockchain Security, Kidney Disease Diagnosis, Medical Image Analysis, Privacy Preservation, Deep Learning, Convolutional Neural Network, Elliptic Curve Cryptography
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-06

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Privacy-preserving kidney image analysis via blockchain-secured federated deep learning framework

Share this