  Federated learning enables collaborative training of machine learning models
by keeping the raw data of the involved workers private. Three of its main
objectives are to improve the models' privacy, security, and scalability.
Vertical Federated Learning (VFL) offers an efficient cross-silo setting where
a few parties collaboratively train a model without sharing the same features.
In such a scenario, classification labels are commonly considered sensitive
information held exclusively by one (active) party, while other (passive)
parties use only their local information. Recent works have uncovered important
flaws of VFL, leading to possible label inference attacks under the assumption
that the attacker has some, even limited, background knowledge on the relation
between labels and data. In this work, we are the first (to the best of our
knowledge) to investigate label inference attacks on VFL using a
zero-background knowledge strategy. To formulate our proposal, we focus on
Graph Neural Networks (GNNs) as a target model for the underlying VFL. In
particular, we refer to node classification tasks, which are widely studied,
and GNNs have shown promising results. Our proposed attack, BlindSage, provides
impressive results in the experiments, achieving nearly 100% accuracy in most
cases. Even when the attacker has no information about the used architecture or
the number of classes, the accuracy remains above 90% in most instances.
Finally, we observe that well-known defenses cannot mitigate our attack without
affecting the model's performance on the main classification task.
