Abstract:
ML algorithms play a vital role in health data analytics in such a way that
medical institutions and health practitioners can use them in exploring big data
and identifying health trends essential to public health knowledge which would
be impossible to do manually. That being said, tree-based algorithms are widely
adopted in the healthcare sector especially now that the sector is gradually leaning
towards digital transformation. Since they are white box models, they are
easy to understand for medical professionals who are not knowledgeable of ML.
However, the issue of security and privacy for medical data in health analytics
remains prevalent. Data privacy must be preserved in settings when ML is executed
in an outsourced ML service provider which may have access in case of
unintended data leakage. Hence, our study aims to provide a method for implementing
privacy-preserving tree-based ML algorithms by incorporating FHE on
medical data, particularly using Concrete ML in a client-server system.
