ESE: Efficient Security Enhancement Method for the Secure Aggregation Protocol in Federated Learning

In federated learning, a parameter server may actively infer sensitive data of users and a user may arbitrarily drop out of a learning process. Bonawitz et al. propose a secure aggregation protocol for federated learning against a semi-honest adversary and a security enhancement method against an active adversary at ACM CCS 2017. The purpose of this paper is to analyze their security enhancement method and to design an alternative. We point out that their security enhancement method has the risk of Eclipse attack and that the consistency check round in their method could be removed. We give a new efficient security enhancement method by redesigning an authentication message and by adjusting the authentication timing. The new method produces an secure aggregation protocol against an active adversary with less communication and computation costs.