Name
Abstract | ||
|---|---|---|
•Data mining, accompanied with information dissemination can lead to a privacy breach.•Utility and privacy often appear as conflicting factors in the existing privacy-preserving approaches.•An efficient perturbation paradigm is proposed to provide enough balance between privacy and utility.•A new privacy model is proposed to guarantee optimal perturbation parameter selection.•The proposed data perturbation approach excels in speed, scalability, and accuracy for big data perturbation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.05.053 | Information Sciences |
Keywords | DocType | Volume |
Information privacy,Privacy-preserving data mining,Big data privacy,Data perturbation,Big data | Journal | 527 |
ISSN | Citations | PageRank |
0020-0255 | 2 | 0.36 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahawaga Arachchige Pathum Chamikara | 1 | 11 | 3.88 |
| Peter Bertók | 2 | 158 | 35.62 |
| Dongxi Liu | 3 | 312 | 40.40 |
| Seyit Ahmet Çamtepe | 4 | 109 | 11.97 |
| Ibrahim Khalil | 5 | 104 | 14.91 |