Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning

Andrei Semenov, Zmushko Philip, Alexander Pichugin, Aleksandr Beznosikov

Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning: 2 upvotes on Hugging Face Daily Papers, #24 of 28 papers on 2024-12-17. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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