Request a trial

The Apheris Resource Library

Gain insights in establishing collaborative data ecosystems, building federated ML and analytics infrastructure, and unlocking greater value from data.

Featured Resources

Guide
Guide to Collaborative Data Ecosystems
White Paper
Beyond MLOps - How Secure Data Collaboration Unlocks the Next Frontier of AI Innovation
Guide
Buyer's Guide to Secure Data Collaboration
Guide
Guide to Collaborative Data Ecosystems

Latest Blog Posts

See all
Article
Data collaboration vs data sharing
It's often assumed that data sharing is required in order to derive value from data held across multiple organizations in a value chain. However, data sharing comes with challenges. Working with federated data, however, addresses these challenges and allows for better collaboration across organizational boundaries.
Article
The Five Qualities of Collaborative Data Ecosystems
Over the past decade, organizations have focused heavily on implementing the culture, tools, and processes to create value from data with data science and machine learning within the enterprise. But this transformation doesn't intend to stop at corporate boundaries. Read the article to find out more
Article
7 Value Drivers of Complementary Data - Your Path to Unlocking the Benefits of Federated Data
There is only value in data if it can be used, and if there is appropriate access to that data when it is needed. Learn how you can turn decentralized, federated data into a complementary strategic data asset.
Article
Data collaboration vs data sharing
It's often assumed that data sharing is required in order to derive value from data held across multiple organizations in a value chain. However, data sharing comes with challenges. Working with federated data, however, addresses these challenges and allows for better collaboration across organizational boundaries.

Case Studies

Case Study
Case Study: Pharma
Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.
Case Study
Case Study: Healthcare
We need to make biomedical data easily accessible and securely usable, to find new medicines and diagnostics, and to establish data-based insights that evolve the practice of medicine.
Case Study
Case Study: Manufacturing
Learn how multiple companies across a manufacturing value chain have securely collaborated on data while protecting the IP of any party, data, or model.
Case Study
Case Study: Pharma
Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

Publications

Publication

Security of AI  Systems: Fundamentals

Advising the German Federal Office for Information Security on the Security of AI-Systems, Apheris provides an overview on attack vectors and threats of AI systems where external data is used or trained models are exposed to third parties. Recommendations are derived on how to systematically safeguard and test AI-systems.

Publication

Asymmetric Private Set Intersection and Private Vertical Federated Machine Learning

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting.

Publication

PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device.

Publication

Security of AI  Systems: Fundamentals

Advising the German Federal Office for Information Security on the Security of AI-Systems, Apheris provides an overview on attack vectors and threats of AI systems where external data is used or trained models are exposed to third parties. Recommendations are derived on how to systematically safeguard and test AI-systems.