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.
Privacy Data Management and Awareness for Public Administrations: a Case Study from the Healthcare Domain
AFP2017 - ENISA Annual Privacy Forum 2017, 7 – 8 June 2017, Vienna