Privacy-preserving biometric verification with outsourced correlation filter computation

2021 ◽  
Author(s):  
Motahareh Taheri ◽  
Saeed Mozaffari ◽  
Parviz Keshavarzi
Keyword(s):  
Privacy Preserving ◽  
Correlation Filter ◽  
Biometric Verification

scholarly journals Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

Computers ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Milad Salem ◽  
Shayan Taheri ◽  
Jiann-Shiun Yuan
Keyword(s):  
Transfer Learning ◽  
Search Algorithm ◽  
Homomorphic Encryption ◽  
Recognition System ◽  
Privacy Preserving ◽  
Modern World ◽  
Biometric Data ◽  
Liveness Detection ◽  
Biometric Verification ◽  
Feature Extractor

Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

Privacy Preserving Biometric Verification

2014 ◽  
Vol 3 (1) ◽  
pp. 6-8
Author(s):  
Angarika Jadhav ◽  
Prof. N.M. Shahane
Keyword(s):  
Privacy Preserving ◽  
Biometric Verification

Privacy Preserving Public Auditing in Secured Cloud Storage

2012 ◽  
Vol 3 (3) ◽  
pp. 60-61
Author(s):  
V.Sajeev V.Sajeev ◽  
◽  
R.Gowthamani R.Gowthamani
Keyword(s):  
Cloud Storage ◽  
Privacy Preserving ◽  
Public Auditing

Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

2018 ◽  
Vol E101.A (1) ◽  
pp. 138-148 ◽  
Author(s):  
Haruna HIGO ◽  
Toshiyuki ISSHIKI ◽  
Kengo MORI ◽  
Satoshi OBANA
Keyword(s):  
Privacy Preserving ◽  
Hill Climbing

A Survey of Auditing Techniques for Privacy Preserving and Ensure Data Correctness in the Cloud

2017 ◽  
Vol 7 (1) ◽  
pp. 181-185 ◽  
Author(s):  
S Durga Bhavani ◽  
◽  
Gudlanarva Sudhakar ◽  
Mohammad Almechal ◽  
◽  
...  
Keyword(s):  
Privacy Preserving
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