A Secure Framework to Preserve Privacy of Biometric Templates on Cloud Using Deep Learning

Title:A Secure Framework to Preserve Privacy of Biometric Templates on Cloud Using Deep Learning

Volume: 14 Issue: 5

Author(s): Shefali Arora*M.P.S. Bhatia

Affiliation:

• Division of Computer Engineering, Netaji Subhas Institute of Technology, Delhi,India

Keywords: Cloud, cryptography, templates, biometrics, privacy, salting.

Abstract:

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.

Methods: In this paper, we improvise a secure framework named DeepCrypt that can be applied to protect biometric templates during the 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 it 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.

Conclusion: The proposed method is robust and provides cancellable biometric templates, high security and better matching performance as compared to traditional techniques used to protect the biometric template.

Cite this article as:

Arora Shefali *, Bhatia M.P.S., A Secure Framework to Preserve Privacy of Biometric Templates on Cloud Using Deep Learning, Recent Advances in Computer Science and Communications 2021; 14 (5) . https://dx.doi.org/10.2174/2666255813999200724172343