A novel pipeline for privacy-preserving of medical data using federated learning and blockchain
The application of machine learning (ML) in healthcare has the potential to revolutionize the way patients are diagnosed, treated, and monitored. The capability of ML algorithms to process and analyze vast amounts of data has resulted in the development of new diagnostic and treatment tools that can...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The application of machine learning (ML) in healthcare has the potential to revolutionize the way patients are diagnosed, treated, and monitored. The capability of ML algorithms to process and analyze vast amounts of data has resulted in the development of new diagnostic and treatment tools that can enhance patient outcomes. However, machine learning 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 blockchain with federated learning to guarantee the privacy, security, and integrity of the model trained on patient data. The results of the conducted experiments demonstrate the performance and effectiveness of the proposed pipeline. |
---|---|
ISSN: | 0094-243X 1551-7616 |
DOI: | 10.1063/5.0220674 |