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.
Self-Sovereign Identity: Realising the journey from innovation to market
Atos Digital Security Magazine
Self-Sovereign Identity can be considered as the most significant paradigm shift, surpassing the more established centralised and federated approaches that have shaped identity management in the past decades.
An Overview on ARIES: Reliable European Identity Ecosystem
Chapter in book “Challenges in Cybersecurity and Privacy - the European Research Landscape” by River Publishers.
ISBN: 9788770220880
doi: https://doi.org/10.13052/rp-9788770220873
Data protection in the era of artificial intelligence. Trends, existing solutions and recommendations for privacy-preserving technologies
Published by Big Data Value Association (BDVA)
ARIES project
Chapter 11 in book “Challenges in Cybersecurity and Privacy – the European Research Landscape”, ISBN: 978-87-7022-088-0
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