  This paper proposes a sensor data anonymization model that is trained on
decentralized data and strikes a desirable trade-off between data utility and
privacy, even in heterogeneous settings where the sensor data have different
underlying distributions. Our anonymization model, dubbed Blinder, is based on
a variational autoencoder and one or multiple discriminator networks trained in
an adversarial fashion. We use the model-agnostic meta-learning framework to
adapt the anonymization model trained via federated learning to each user's
data distribution. We evaluate Blinder under different settings and show that
it provides end-to-end privacy protection on two IMU datasets at the cost of
increasing privacy loss by up to 4.00% and decreasing data utility by up to
4.24%, compared to the state-of-the-art anonymization model trained on
centralized data. We also showcase Blinder's ability to anonymize the radio
frequency sensing modality. Our experiments confirm that Blinder can obscure
multiple private attributes at once, and has sufficiently low power consumption
and computational overhead for it to be deployed on edge devices and
smartphones to perform real-time anonymization of sensor data.
