The Apheris Resource Library

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



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.


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.


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.