  Federated learning promises to make machine learning feasible on distributed,
private datasets by implementing gradient descent using secure aggregation
methods. The idea is to compute a global weight update without revealing the
contributions of individual users. Current practical protocols for secure
aggregation work in an "honest but curious" setting where a curious adversary
observing all communication to and from the server cannot learn any private
information assuming the server is honest and follows the protocol. A more
scalable and robust primitive for privacy-preserving protocols is shuffling of
user data, so as to hide the origin of each data item. Highly scalable and
secure protocols for shuffling, so-called mixnets, have been proposed as a
primitive for privacy-preserving analytics in the Encode-Shuffle-Analyze
framework by Bittau et al., which was later analytically studied by Erlingsson
et al. and Cheu et al.. The recent papers by Cheu et al., and Balle et al. have
given protocols for secure aggregation that achieve differential privacy
guarantees in this "shuffled model". Their protocols come at a cost, though:
Either the expected aggregation error or the amount of communication per user
scales as a polynomial $n^{\Omega(1)}$ in the number of users $n$. In this
paper we propose simple and more efficient protocol for aggregation in the
shuffled model, where communication as well as error increases only
polylogarithmically in $n$. Our new technique is a conceptual "invisibility
cloak" that makes users' data almost indistinguishable from random noise while
introducing zero distortion on the sum.
