  We present a Federated Learning (FL) based solution for building a
distributed classifier capable of detecting URLs containing GDPR-sensitive
content related to categories such as health, sexual preference, political
beliefs, etc. Although such a classifier addresses the limitations of previous
offline/centralised classifiers,it is still vulnerable to poisoning attacks
from malicious users that may attempt to reduce the accuracy for benign users
by disseminating faulty model updates. To guard against this, we develop a
robust aggregation scheme based on subjective logic and residual-based attack
detection. Employing a combination of theoretical analysis, trace-driven
simulation, as well as experimental validation with a prototype and real users,
we show that our classifier can detect sensitive content with high accuracy,
learn new labels fast, and remain robust in view of poisoning attacks from
malicious users, as well as imperfect input from non-malicious ones.
