  Vertical Federated Learning (VFL) is a category of Federated Learning in
which models are trained collaboratively among parties with vertically
partitioned data. Typically, in a VFL scenario, the labels of the samples are
kept private from all the parties except for the aggregating server, that is
the label owner. Nevertheless, recent works discovered that by exploiting
gradient information returned by the server to bottom models, with the
knowledge of only a small set of auxiliary labels on a very limited subset of
training data points, an adversary can infer the private labels. These attacks
are known as label inference attacks in VFL. In our work, we propose a novel
framework called KDk, that combines Knowledge Distillation and k-anonymity to
provide a defense mechanism against potential label inference attacks in a VFL
scenario. Through an exhaustive experimental campaign we demonstrate that by
applying our approach, the performance of the analyzed label inference attacks
decreases consistently, even by more than 60%, maintaining the accuracy of the
whole VFL almost unaltered.
