Abstract
With the increasing number of users enrolled, biometric identification requires more computing resources to scan all records of a database and locate the best match. As such, database owners are willing to delegate user biometric information (in encrypted state) to the cloud to enroll and identify users, while preserving privacy. Wang et al. proposed a cloud-based privacy-preserving biometric scheme, a.k.a. CloudBI, in ESORICS 2015, but their security assumption does not capture practical aspects of real world attacks. In this paper, we show how an attack enrolls fake biometric data and then manipulates them to recover encrypted an identification request in CloudBI. Next, we propose an effective security patch to CloudBI, which is secure against enrollment-level attackers. Experimental results show that the proposed security patch bring about little performance degradation to CloudBI.
| Original language | English |
|---|---|
| Title of host publication | ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks |
| Publisher | IEEE Computer Society |
| Pages | 595-600 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509047499 |
| DOIs | |
| Publication status | Published - 2017 Jul 26 |
| Event | 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 - Milan, Italy Duration: 2017 Jul 4 → 2017 Jul 7 |
Publication series
| Name | International Conference on Ubiquitous and Future Networks, ICUFN |
|---|---|
| ISSN (Print) | 2165-8528 |
| ISSN (Electronic) | 2165-8536 |
Other
| Other | 9th International Conference on Ubiquitous and Future Networks, ICUFN 2017 |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 17/7/4 → 17/7/7 |
Fingerprint
Keywords
- Biometrics
- Cloud
- Identification
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
Cite this
Cloud-based biometrics processing for privacy-preserving identification. / Hahn, Changhee; Shin, Hyungjune; Hur, Junbeom.
ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2017. p. 595-600 7993859 (International Conference on Ubiquitous and Future Networks, ICUFN).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cloud-based biometrics processing for privacy-preserving identification
AU - Hahn, Changhee
AU - Shin, Hyungjune
AU - Hur, Junbeom
PY - 2017/7/26
Y1 - 2017/7/26
N2 - With the increasing number of users enrolled, biometric identification requires more computing resources to scan all records of a database and locate the best match. As such, database owners are willing to delegate user biometric information (in encrypted state) to the cloud to enroll and identify users, while preserving privacy. Wang et al. proposed a cloud-based privacy-preserving biometric scheme, a.k.a. CloudBI, in ESORICS 2015, but their security assumption does not capture practical aspects of real world attacks. In this paper, we show how an attack enrolls fake biometric data and then manipulates them to recover encrypted an identification request in CloudBI. Next, we propose an effective security patch to CloudBI, which is secure against enrollment-level attackers. Experimental results show that the proposed security patch bring about little performance degradation to CloudBI.
AB - With the increasing number of users enrolled, biometric identification requires more computing resources to scan all records of a database and locate the best match. As such, database owners are willing to delegate user biometric information (in encrypted state) to the cloud to enroll and identify users, while preserving privacy. Wang et al. proposed a cloud-based privacy-preserving biometric scheme, a.k.a. CloudBI, in ESORICS 2015, but their security assumption does not capture practical aspects of real world attacks. In this paper, we show how an attack enrolls fake biometric data and then manipulates them to recover encrypted an identification request in CloudBI. Next, we propose an effective security patch to CloudBI, which is secure against enrollment-level attackers. Experimental results show that the proposed security patch bring about little performance degradation to CloudBI.
KW - Biometrics
KW - Cloud
KW - Identification
UR - http://www.scopus.com/inward/record.url?scp=85028029702&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028029702&partnerID=8YFLogxK
U2 - 10.1109/ICUFN.2017.7993859
DO - 10.1109/ICUFN.2017.7993859
M3 - Conference contribution
AN - SCOPUS:85028029702
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 595
EP - 600
BT - ICUFN 2017 - 9th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
ER -