تقرير
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
| العنوان: | Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning |
|---|---|
| المؤلفون: | Zhao, Joshua C., Dabholkar, Ahaan, Sharma, Atul, Bagchi, Saurabh |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this, prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However, despite demonstrating the effectiveness of these attacks in breaching privacy, prior work has not investigated the usefulness of the reconstructed data for downstream tasks. In this work, we explore data reconstruction attacks through the lens of training and improving models with leaked data. We demonstrate the effectiveness of both GI and LLL attacks in maliciously training models using the leaked data more accurately than a benign federated learning strategy. Counter-intuitively, this bump in training quality can occur despite limited reconstruction quality or a small total number of leaked images. Finally, we show the limitations of these attacks for downstream training, individually for GI attacks and for LLL attacks. Comment: Accepted to CVPR 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.18144 |
| رقم الأكسشن: | edsarx.2403.18144 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |