Privacy-preserving Data Science on QSAR models

This case study demonstrates the utility and effectiveness of Apheris’ platform and services for data science on not directly accessible and distributed data. An integral part of our platform is the implementation of federated learning, which decentralizes the learning algorithms to access a number of different data sets while maintaining privacy. This process eliminates the need to pool sensitive data, which has been a major hurdle in practice due to a lack of trust between different data providers.

In this white paper, you will learn...

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How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data

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Sowcase of federated learning, privacy testing and other tools that enable data science practices on not directly accessible and distributed data, at the example of QSAR models.

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