Preserving data privacy in Machine Learning pipelines with Federated Learning
FAME's project blog
The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.
Towards a Privacy-preserving Reliable European Identity Ecosystem
Annual Privacy Forum 2017
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10518)
Orchestrating Privacy Enhancing Technologies and Services with BPM Tools. The WITDOM Data Protection Orchestrator.
ARES '17 Proceedings of the 12th International Conference on Availability, Reliability and Security
Article No. 89