Name
Title | ||
|---|---|---|
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security. |
Abstract | ||
|---|---|---|
Deep learning methods can play a crucial role in anomaly detection, prediction, and supporting decision making for applications like personal health-care, pervasive body sensing, and so on. However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction. This... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIFS.2017.2763126 | IEEE Transactions on Information Forensics and Security |
Keywords | DocType | Volume |
Servers,Data models,Data privacy,Encryption,Training,Computer architecture | Journal | 13 |
Issue | ISSN | Citations |
3 | 1556-6013 | 1 |
PageRank | References | Authors |
0.35 | 15 | 4 |