Name
Abstract | ||
|---|---|---|
Homomorphic encryption is a significant method to protect user privacy in cloud computing environment. Due to the computation efficiency issue, there is still not many homomorphic encryption applications for common users. In this paper,we try to use homomorphic encryption to enhance the privacy in cloud-based face recognition system. By balancing the workload between client and server,and reimplementing the similarity measurement function, our homomorphic encryption version’s performance is almost the same as the original version in terms of accuracy and time consumption. Our work is especially beneficial to many face recognition methods that are using Euclidian distance as their similarity metric. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11063-021-10477-y | Neural Processing Letters |
Keywords | DocType | Volume |
Homomorphic encryption, Face recognition | Journal | 54 |
Issue | ISSN | Citations |
4 | 1370-4621 | 1 |
PageRank | References | Authors |
0.35 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Yang | 1 | 160 | 76.32 |
| Yuhang Zhang | 2 | 1 | 0.35 |
| Jie Sun | 3 | 1 | 0.35 |
| Xun Wang | 4 | 104 | 26.30 |