Name
Abstract | ||
|---|---|---|
Empowered by the high connectivity of manifold devices in today's world, distributed machine learning enables multiple, distributed users to build a joint model by sharing their gradients over a network. In this paper, we highlight the privacy risk of sharing gradients by proposing LLG, an algorithm to disclose the labels of the users' training data from their shared gradients. We conduct an empirical analysis on two datasets to demonstrate the validity of our algorithm. Results show that our approach effectively extracts the labels with high accuracy in different scenarios. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCNC49032.2021.9369498 | 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) |
Keywords | DocType | ISSN |
Distributed machine learning,privacy,leakage from gradients | Conference | 2331-9852 |
ISBN | Citations | PageRank |
978-1-7281-9795-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aidmar Wainakh | 1 | 7 | 3.13 |
| Till Müßig | 2 | 0 | 0.34 |
| Tim Grube | 3 | 19 | 7.54 |
| Max Mühlhäuser | 4 | 1652 | 252.87 |