MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud

2021 ◽  
Author(s):  
Xiaopeng Yang ◽  
Hui Zhu ◽  
Fengwei Wang ◽  
Songnian Zhang ◽  
Rongxing Lu ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Biometric Identification

scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaopeng Yang ◽  
Hui Zhu ◽  
Songnian Zhang ◽  
Rongxing Lu ◽  
Xuesong Gao
Keyword(s):  
Homomorphic Encryption ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Large Dataset ◽  
Biometric Data ◽  
Communication Costs ◽  
Identification Scheme ◽  
Biometric Template ◽  
Cloud Servers

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

scholarly journals An Efficient and Privacy-Preserving Biometric Identification Scheme in Cloud Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 19025-19033 ◽  
Author(s):  
Liehuang Zhu ◽  
Chuan Zhang ◽  
Chang Xu ◽  
Ximeng Liu ◽  
Cheng Huang
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Biometric Identification ◽  
Identification Scheme

PEBIID: Privacy-preserving and Efficient Biometric Identification for IoV DApp

2021 ◽  
Author(s):  
Chun Liu ◽  
Lin Yang ◽  
LinRu Ma ◽  
LiuCheng Shi ◽  
Xuexian Hu ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Biometric Identification

CloudBI: Practical Privacy-Preserving Outsourcing of Biometric Identification in the Cloud

2015 ◽  
pp. 186-205 ◽  
Author(s):  
Qian Wang ◽  
Shengshan Hu ◽  
Kui Ren ◽  
Meiqi He ◽  
Minxin Du ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Biometric Identification
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