Summary of Just a Simple Transformation Is Enough For Data Protection in Vertical Federated Learning, by Andrei Semenov et al.
Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
by Andrei Semenov, Philip Zmushko, Alexander Pichugin, Aleksandr Beznosikov
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores Vertical Federated Learning (VFL), a method for collaborative training of deep learning models while maintaining privacy protection. However, the authors identify components vulnerable to attacks by malicious parties, focusing on feature reconstruction attacks that target input data compromise. They theoretically demonstrate that these attacks cannot succeed without knowledge of the prior distribution on data and experimentally confirm their findings using simple model architecture transformations. The results show that MLP-based models are resistant to state-of-the-art feature reconstruction attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping private information safe when many machines work together to learn from data. They want to make sure bad actors can’t get the information they’re trying to keep secret. To do this, they test a type of attack that tries to figure out what’s in the data by looking at how it changes during training. The authors show that if you change the way the model is set up just a little bit, these attacks won’t work. They found that using a certain kind of model makes it really hard for attackers to get the information they’re trying to steal. |
Keywords
» Artificial intelligence » Deep learning » Federated learning
