Name
Title | ||
|---|---|---|
Securing Collaborative Deep Learning in Industrial Applications Within Adversarial Scenarios. |
Abstract | ||
|---|---|---|
Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imp... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2853676 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Cryptography,Machine learning,Data privacy,Collaboration,Training data,Informatics | Software deployment,Cryptography,Computer security,Computer science,Real-time computing,Exploit,Game theory,Artificial intelligence,Access control,Deep learning,Information privacy,Energy consumption | Journal |
Volume | Issue | ISSN |
14 | 11 | 1551-3203 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Esposito | 1 | 569 | 54.78 |
| Xin Su | 2 | 21 | 9.92 |
| Shadi Aljawarneh | 3 | 233 | 27.16 |
| Chang Choi | 4 | 261 | 39.04 |