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Abstract/Summary
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The Semantic Web was built for interoperability; for combining and sharing data. The
reality is unfortunately that not all data can be shared as-is. Healthcare data is an obvious example due to its privacy-sensitive nature, but other organisations and individuals in general are becoming more aware of the sensitivity and practical problems of sharing data. Additionally the amount of data is increasing exponentially and we need help analysing and unlocking the potential of these data, which will allow for a lot of knowledge and insights to be discovered. The combination of semantic data with Federated Analysis (FA) as described in the Personal Health Train manifesto, will enable machine actionability and re-use of data; the main goal of the FAIR principles. FA techniques (e.g. federated learning, multiparty computation) are rapidly becoming more and more proficient in solving this problem by expanding the ways we can share insights and models without having to share sensitive data. FA is showing a way towards secure and ethical big data analytics, where sensitive data does not need to travel, but allows models to learn from data sets without compromising on privacy and security.
Now you know the why, let’s explain the how: In this 4 hour crash course, we will present an open source federated analysis architecture and a real world usecase. This practical application of the Personal Health Train concept will show how federated data analysis can benefit patients, clinicians and researchers. And hopefully also you!
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