Eluding Secure Aggregation in Federated Learning via Model Inconsistency

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks. In this work, we show that a malicious server can easily elude secure aggregation as if the latter were not in place. We devise two different attacks capable of inferring information on individual private training datasets, independently of the number of users participating in the secure aggregation. This makes them concrete threats in large-scale, real-world federated learning applications. The attacks are generic and do not target any specific secure aggregation protocol. They are equally effective even if the secure aggregation protocol is replaced by its ideal functionality that provides the perfect level of security. Our work demonstrates that secure aggregation has been incorrectly combined with federated learning and that current implementations offer only a “false sense of security”.