Name
Abstract | ||
|---|---|---|
Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic mon... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TII.2019.2936825 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Correlation,Differential privacy,Sensitivity,Feature extraction,Machine learning,Machine learning algorithms | Differential privacy,Feature selection,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 3 | 1551-3203 |
Citations | PageRank | References |
3 | 0.41 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhang | 1 | 868 | 78.75 |
| Tianqing Zhu | 2 | 159 | 27.73 |
| Ping Xiong | 3 | 80 | 10.10 |
| Huan Huo | 4 | 35 | 10.00 |
| Zahir Tari | 5 | 2409 | 368.61 |
| Wanlei Zhou | 6 | 2295 | 189.31 |