Name
Abstract | ||
|---|---|---|
Biometrics-based cryptosystems are helpful tools facilitating our daily life. Examples are that people use their biometrics to identify themselves in clouds or protect passwords as well as other privacy information in mobile phones connected to Internet-of-Things. Among various biometrics, fingerprints are one used most widely. A promising type of secure cryptosystems based on fingerprints is the so called fuzzy fingerprint vaults which can bind a secret key to one fingerprint template and release the same key later with the help of a similar but not necessarily fully identical fingerprint template. However, some aspects such as the security and matching accuracy of existing fingerprint-based fuzzy vaults are still not satisfactory due to troublesome issues such as the alignment of fingerprints and minutiae quantization. |
Year | Venue | Field |
|---|---|---|
2017 | Inscrypt | Pattern recognition,Minutiae,Computer science,Fuzzy logic,Cryptosystem,Theoretical computer science,Fingerprint,Biometric cryptosystems,Password,Artificial intelligence,Biometrics,Quantization (signal processing) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangmin Li | 1 | 0 | 0.34 |
| Ning Ding | 2 | 50 | 8.35 |
| Haining Lu | 3 | 12 | 1.96 |
| Dawu Gu | 4 | 644 | 103.50 |
| Shanshan Wang | 5 | 150 | 27.21 |
| beibei xu | 6 | 77 | 5.56 |
| Yuan Yuan | 7 | 301 | 26.63 |
| Siyun Yan | 8 | 0 | 0.34 |