  The COVID19 pandemic had enormous economic and societal consequences. Contact
tracing is an effective way to reduce infection rates by detecting potential
virus carriers early. However, this was not generally adopted in the recent
pandemic, and privacy concerns are cited as the most important reason. We
substantially improve the privacy guarantees of the current state of the art in
decentralized contact tracing. Whereas previous work was based on statistical
inference only, we augment the inference with a learned neural network and
ensure that this neural augmentation satisfies differential privacy. In a
simulator for COVID19, even at epsilon=1 per message, this can significantly
improve the detection of potentially infected individuals and, as a result of
targeted testing, reduce infection rates. This work marks an important first
step in integrating deep learning into contact tracing while maintaining
essential privacy guarantees.
