Privacy Of Biometric Templates Research Articles

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

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 the biometric template.

Partial DCT-based cancelable biometric authentication with security and privacy preservation for IoT applications

Biometric authentication is being vastly used in identity verification for several IoT applications, nowadays. The security and privacy of the biometrics templates used in the authentication process becomes mandatory as it contains rich personal information of the user. Cancelable Biometric System (CBS) is a template securing approach using repeated distortions/transformations at feature/signal level. It is an effective template securing approach which provides non-invertibility, revocability and diversity to the users enrolled with the cancelable template database. The cancelable template database containing cancelable biometric templates of the users is typically stored in a trusted standalone server. To meet the growing demand of IoT applications, cloud computing with provision to store and process even large database, is being largely deployed. However, the security and privacy of the data stored in the cloud is uncertain. In this paper, we have proposed a novel partial DCT-based CBS with privacy and security preservation for providing authentication services in various IoT applications. We have also devised techniques for session key agreement, data encryption and data integrity in the proposed framework to solve the issues of confidentiality, integrity, and availability (CIA) during cancelable template enrolment and authentication. The experimental results and analysis prove that proposed approach performs user authentication with high accuracy and minimal overhead while preserving security and privacy of sensitive cancelable biometric templates.

Deep Secure Quantization: On secure biometric hashing against similarity-based attacks

Abstract The widespread application of biometric recognition has emerged solid protection on the privacy of biometric templates. Non-invertible transformations such as random projections are popular solutions for this purpose, yet their security has been recently challenged due to the advent of similarity-based attacks (SA). To address this issue, we developed Deep Secure Quantization (DSQ), a new biometric hashing scheme for privacy-preserving biometric recognition. DSQ essentially takes into account the information leakage between the original distance and the hashed distance, which is the security blind spot of existing hashing models. This leakage is further incorporated into an optimal hashing objective which well balances between security and utility. Hashing is then modeled as a highly nonlinear problem solved by a novel deep neural network. Experiments on CASIA-v4-interval demonstrate that DSQ not only offers strong resistance to SA but also yields comparable or even superior recognition performance over existing biometric hashing methods, including deep framework-based ones.

A biometric-based authenticated key agreement scheme for session initiation protocol in ip-based multimedia networks

Session Initial Protocol (SIP) has been widely adopted for signaling and controlling interactive sessions in multimedia communication networks. Despite its various advantages compared to predecessor protocols, the security and privacy of the SIP remain challenges due to the risk of real-world public networks. While most SIP applications utilize end-to-end communications, existing studies mainly focus on client-server protocols. In this study, we propose a novel SIP authenticated key agreement protocol for all user-server, user-user, and group communications. An end user employs a short-term token to communicate with either end-users or multimedia servers without connecting to a trusted server. Our security analyzes show that the scheme not only resists all known attacks, but provides the system with many desirable features, including direct end-to-end communications, preserving biometric template privacy, user access control, smart card revocation, and long-term secret updates. The latency of the authenticated key agreement phase is relatively small, and thus this signaling protocol is appropriate for a wide range of real-time applications.