title: Just a Simple Transformation is Enough for Data Protection in Vertical
    Federated Learning
summary: 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.
paper_authors._id: 67614df02541e5e5d18f0ebf
paper_authors.name: Andrei Semenov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-17T10:27:49.157Z
paper_authors._id: 6479f8335f3450e1ded40774
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/iOjE2dcSUS-XYE0WJe6U8.jpeg
paper_authors.fullname: Andrei Semenov
paper_authors.type: user
paper_authors.user: Andron00e
paper_authors._id: 67614df02541e5e5d18f0ec0
paper_authors.name: Philip Zmushko
paper_authors._id: 67614df02541e5e5d18f0ec1
paper_authors.name: Alexander Pichugin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-18T12:39:06.224Z
paper_authors._id: 6762c20ae4260571f17da2b4
paper_authors.avatarUrl: /avatars/0f8f0bf006f9a6e3df7b09663c6044d2.svg
paper_authors.fullname: Alexander Pichugin
paper_authors.type: user
paper_authors.user: pichlex
paper_authors._id: 67614df02541e5e5d18f0ec2
paper_authors.name: Aleksandr Beznosikov
paper_title: Just a Simple Transformation is Enough for Data Protection in Vertical
    Federated Learning
paper_summary: 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.
paper_ai_summary: MLP-based models in vertical federated learning are resistant to feature reconstruction attacks due to model architecture transformations enhancing data privacy.
paper_ai_keywords: vertical federated learning feature reconstruction attacks prior distribution model architecture transformations MLP-based models