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
Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.
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
This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.
In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.
How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.