The article discusses the implementation of machine learning and AI algorithms in a clinical setting, particularly for brain lesion segmentation. It emphasizes federated learning to ensure patient data is processed securely within hospitals. While algorithms are considered company assets, safeguarding them from theft and unauthorized distribution is crucial, especially in environments managed by third-party IT administrators. The article outlines comprehensive protection measures across three layers: algorithm code protection, runtime environment risks, and deployment environment safeguards. The goal is to mitigate risks of intellectual property theft and reverse engineering by restricting administrator access and capabilities.
We aim to advance ML & AI Algorithms to enable brain lesion segmentation at hospital/clinic locations, ensuring data is processed securely through federated learning.
We need to protect not only sensitive data but also secure algorithms in a heterogeneous federated environment with comprehensive protection measures.
Protection measures are categorized into two types: preventing administrator users from accessing and executing algorithms, and blocking them from reverse engineering the code.
Our approach addresses three critical layers of protection: algorithm code protection, runtime environment evaluation, and deployment environment safeguards against unauthorized access.
#ai-in-healthcare #federated-learning #data-security #intellectual-property #machine-learning-algorithms
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