Name
Abstract | ||
|---|---|---|
Due to the increasing intelligence of data acquisition and analysis in cyber physical systems (CPSs) and the emergence of various transmission vulnerabilities, this paper proposes a differential privacy protection method for frequent pattern mining in view of the application-level privacy protection requirements of industrial interconnected systems. This method designs a low-cohesion algorithm to realize differential privacy protection. In the implementation of differential privacy protection, Top-k frequent mode method is introduced, which combines the factors of index mechanism and low cohesive weight of each mode, and the original support of each selected mode is disturbed by Laplacian noise. It achieves a balance between privacy protection and utility, guarantees the trust of all parties in CPS and provides an effective solution to the problem of privacy protection in industrial Internet systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11227-019-03122-y | The Journal of Supercomputing |
Keywords | DocType | Volume |
Differential privacy protection, Industrial Internet, CPS | Journal | 76 |
Issue | ISSN | Citations |
11 | 0920-8542 | 2 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hou Jun | 1 | 52 | 9.26 |
| Li Qian-Mu | 2 | 33 | 14.78 |
| Shicheng Cui | 3 | 7 | 1.82 |
| Shunmei Meng | 4 | 33 | 5.34 |
| Sainan Zhang | 5 | 13 | 3.05 |
| Zhen Ni | 6 | 3 | 1.39 |
| Ye Tian | 7 | 418 | 36.84 |