Just a Simple Transformation is Enough for Data Protection in Vertical
  Federated Learning
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.
67614df02541e5e5d18f0ebf
Andrei Semenov
extracted_confirmed
2024-12-17T10:27:49.157Z
6479f8335f3450e1ded40774
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/iOjE2dcSUS-XYE0WJe6U8.jpeg
Andrei Semenov
user
Andron00e
67614df02541e5e5d18f0ec0
Philip Zmushko
claimed_verified
2025-07-24T09:15:28.406Z
6240eecb585605a407953360
/avatars/8517adfe7ade40faca1afa7791749c66.svg
Zmushko Philip
user
fzmushko
67614df02541e5e5d18f0ec1
Alexander Pichugin
claimed_verified
2024-12-18T12:39:06.224Z
6762c20ae4260571f17da2b4
/avatars/0f8f0bf006f9a6e3df7b09663c6044d2.svg
Alexander Pichugin
user
pichlex
67614df02541e5e5d18f0ec2
Aleksandr Beznosikov
Just a Simple Transformation is Enough for Data Protection in Vertical
  Federated Learning
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.
MLP-based models in vertical federated learning are resistant to feature reconstruction attacks due to model architecture transformations enhancing data privacy.
vertical federated learning feature reconstruction attacks prior distribution model architecture transformations MLP-based models