Just a Simple Transformation is Enough for Data Protection in Vertical
  Federated Learning
Authors: name: Andrei Semenov, name: Philip Zmushko, name: Alexander Pichugin, name: Aleksandr Beznosikov

Vertical Federated Learning (VFL) aims to enable collaborative training of
deep learning models while maintaining privacy protection. However, the VFL
procedure still has components that are vulnerable to attacks by malicious
parties. In our work, we consider feature reconstruction attacks, a common risk
targeting input data compromise. We theoretically claim that feature
reconstruction attacks cannot succeed without knowledge of the prior
distribution on data. Consequently, we demonstrate that even simple model
architecture transformations can significantly impact the protection of input
data during VFL. Confirming these findings with experimental results, we show
that MLP-based models are resistant to state-of-the-art feature reconstruction
attacks.