Paper Title :A Novel Pipeline for Privacy-Preserving of Medical Data using Federated Learning and Block chain

Author :Ansh Sharma, Riju Mukherjee, Subalalitha C.N

Article Citation :Ansh Sharma ,Riju Mukherjee ,Subalalitha C.N , (2023 ) " A Novel Pipeline for Privacy-Preserving of Medical Data using Federated Learning and Block chain " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 1-7, Volume-11,Issue-5

Abstract : The potential of revolutionizing the healthcare sector through the application of machine learning (ML) lies in its ability to diagnose, treat, and monitor patients in innovative ways. With the capability to process vast data amounts, ML algorithms have given rise to novel diagnostic and treatment tools that have the potential to improve patient outcomes. However, ML models are susceptible to various privacy attacks, which can compromise sensitive information. This concern is particularly critical for medical data applications, and as a result, the model itself must be protected against adversarial assaults. Patient privacy is a major concern, and due to privacy issues, high-quality patient data is not accessible online. This paper proposes a pipeline which utilizes multi-key homomorphic encryption and block chain with federated learning so as to guarantee the security, privacy, and integrity of the ML model trained on patient data. The conducted experiments provide evidence of the proposed pipeline's efficiency. Keywords - Federated Learning, Multi-Key Homomorphic Encryption, Blockchain, Privacy, Medical Data.

Type : Research paper

Published : Volume-11,Issue-5

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