Enable safe computational data access for ML

A leading data aggregator in healthcare research partnered with Apheris to provide life sciences customers with granular access to patient data while ensuring GDPR compliance. Apheris enabled fully federated machine learning, optimizing the model training process and enhancing customer satisfaction by addressing long-standing GDPR issues. This partnership illustrates Apheris's potential to unlock data assets securely and efficiently.

Privacy and security considerations are an important part of adopting a federated infrastructure for machine learning and analytics. This whitepaper outlines the various security techniques and privacy controls used by Apheris to safely collaborate and build data applications and AI across organizational and geographical boundaries.

In this whitepaper you will learn about:

The privacy and security landscape, and how Apheris operates within it
Defining attack vectors and threat models as the basis for a privacy and security strategy
Using the 5 Safes as a framework for privacy and security in federated ML and analytics
Practical advice for making the move to a federated data infrastructure

Pharma and biotech companies often lack enough data within their own organizations, limiting AI model accuracy. Read more about my discussions with pharma leaders at this years Drug Discovery Innovation Forum DDIF on overcoming the data access dilemma.