  Wide-scale use of visual surveillance in public spaces puts individual
privacy at stake while increasing resource consumption (energy, bandwidth, and
computation). Neuromorphic vision sensors (event-cameras) have been recently
considered a valid solution to the privacy issue because they do not capture
detailed RGB visual information of the subjects in the scene. However, recent
deep learning architectures have been able to reconstruct images from event
cameras with high fidelity, reintroducing a potential threat to privacy for
event-based vision applications. In this paper, we aim to anonymize
event-streams to protect the identity of human subjects against such image
reconstruction attacks. To achieve this, we propose an end-to-end network
architecture jointly optimized for the twofold objective of preserving privacy
and performing a downstream task such as person ReId. Our network learns to
scramble events, enforcing the degradation of images recovered from the privacy
attacker. In this work, we also bring to the community the first ever
event-based person ReId dataset gathered to evaluate the performance of our
approach. We validate our approach with extensive experiments and report
results on the synthetic event data simulated from the publicly available
SoftBio dataset and our proposed Event-ReId dataset.
