Federated Learning on Vertically Distributed Healthcare Data

If organizations with complimentary data sets could join their databases together, it would become possible to train strong machine learning models that could outcompete current industry standards. However, data sets are protected due to their inherent value. They also often contain intellectual property, which cannot be disclosed or shared with other parties. Furthermore, medical data can contain sensitive and personal identifiable information, which is governed by regulatory frameworks that often prevent companies from easily sharing data within their professional networks.

In this white paper, you will learn...


Everything about vertically distributed data


Workflow of privacy-preserving data science


Deep-dive into privacy-preserving record linkage


Deep-dive into federated and privacy-preserving analytics

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