Name
Abstract | ||
|---|---|---|
•Reviews more than 45 recent solutions papers, and more than 40 different privacy-preserving deep learning techniques.•Proposes a multi-level taxonomy that classifies the privacy-preserving deep learning techniques.•Summarizes evaluation results of the reviewed solutions with respect to performance metrics.•Discusses and outline a number of learned lessons of each privacy-preserving task.•Presents solutions comparison, highlights open research challenges and provides some recommendations for future research. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.11.041 | Neurocomputing |
Keywords | Field | DocType |
Deep learning,Deep neural network,Privacy,Privacy preserving,Sensitive data,Taxonomy | Open research,Collaborative learning,Artificial intelligence,Deep learning,Mathematics,Machine learning,Traditional learning | Journal |
Volume | ISSN | Citations |
384 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amine Boulemtafes | 1 | 0 | 0.34 |
| Abdelouahid Derhab | 2 | 277 | 32.68 |
| Y. Challal | 3 | 176 | 11.33 |