Towards Access Control for Machine Learning Embeddings

Roman Matzutt

Abstract: In this work, we explore the potential to make embeddings, which are becoming an integral part of machine-learning pipelines, shareable with the general public while providing self-contained access control. To this end, we apply attribute-based encryption and discuss a potential application for supply chain management.