Introduction:
Cloud computing involves the use of maximum remote services through a network using minimum
resources via internet. There are various issues associated with cloud computing, such as privacy, security and reliability.
Due to rapidly increasing information on the cloud, it is important to ensure security of user information. Biometric template
security over cloud is one such concern. Leakage of unprotected biometric data can serve as a major risk for the privacy of
individuals and security of real-world applications.
Method:
In this paper, we improvise a secure framework named DeepCrypt, that can be applied to protect biometric
templates during authentication of biometric templates. We use deep Convolutional Neural Networks to extract features
from these modalities. The resulting features are hashed using a secure combination of Blowcrypt (Bcrypt) and SHA-256
algorithm, which salts the templates by default before storing on the server.
Results:
Experiments conducted on the CASIA-Iris-M1-S1, CMU-PIE and FVC-2006 datasets achieve around 99% Genuine
accept rates, proving that this technique helps to achieve better performance along with high template security.
Discussion:
The proposed method is robust and provides cancellable biometric templates, high security and better matching
performance as compared to traditional techniques used to protect biometric template.
Biometric template security
