A privacy preserving online learning framework for medical diagnosis applications

Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning models used at other sites. The work in this thesis utilizes the framework of coordinating the blockchain technology and online training mechanism in order to address the concerns of privacy and security in a methodical manner. Specifically, it integrates online learning with a permissioned blockchain network, using transaction metadata to broadcast a part of models while keeping patient health information private. This framework can treat different types of machine learning models using the same distributed dataset. The study also outlines the advantages and drawbacks of using blockchain technology to tackle the privacy-preserving predictive modeling problem and to improve interoperability amongst institutions. This study implements the proposed solutions for skin cancer diagnosis as a representative case and shows promising results in preserving security and providing high detection accuracy. The experimentation was done on ISIC dataset, and the results were 98.57, 99.13, 99.17 and 97,18 in terms of precision, accuracy, F1-score and recall, respectively.

Grantor dc:publisher

Edith Cowan University, Research Online, Perth, Western Australia

Year dc:date

2022

Author dc:creator

• Nguyen, Trang Pham Ngoc

• Blockchain
• Private blockchain
• Hyperledger fabric
• healthcare
• deep learning
• convolutional neural network
• data privacy
• security
• Electrical and Computer Engineering
• Engineering

Repository record dc:identifier

https://ro.ecu.edu.au/theses/2503

OAI identifier oai:identifier

oai:ro.ecu.edu.au:theses-3505