In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacypreserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsuleembedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques. CCS CONCEPTS • Security and privacy → Biometrics; Privacy-preserving protocols.
In many domains, organizations must model personnel and corresponding data access privileges as fine-grained hierarchical access control models. One class of such models, Role-based Access Control (RBAC) models, has been widely accepted and deployed. However, RBAC models are often used without involving cryptographic keys nor considering confidentiality/privacy at the data level. How to design, implement and dynamically modify such a hierarchy, ensure user and data privacy and distribute and manage necessary cryptographic keys are issues of the utmost importance. One elegant solution for cryptography-based hierarchical access control combines the collusion-resistant and privacy-preserving Access Control Polynomial (ACP) and Atallah's Dynamic and Efficient Extended Key Management scheme. Such a model involves cryptographic keys used to encrypt data, can address confidentiality/privacy at the data level and can efficiently support dynamic changes to the RBAC access hierarchy. In this paper, we discuss several implementation challenges and propose solutions when deploying such a system including: data encryption and decryption, key storage and key distribution. Furthermore, we provide analysis of the efficiency and scalability of the resulting system.
In recent years, biometric, or "who you are," authentication has grown rapidly in acceptance and use. Biometric authentication offers users the convenience of not having to carry a password, PIN, smartcard, etc. Instead, users will use their inherent biometric traits for authentication and, as a result, risk their biometric information being stolen. The security of users' biometric information is of critical importance within a biometric authentication scheme as compromised data can reveal sensitive information: race, gender, illness, etc. A cancellable biometric scheme, the "BioCapsule" scheme, proposed by researchers from Indiana University Purdue University Indianapolis, aims to mask users' biometric information and preserve users' privacy. The BioCapsule scheme can be easily embedded into existing biometric authentication systems, and it has been shown to preserve user-privacy, be resistant to several types of attacks, and have minimal effects on biometric authentication system accuracy. In this research we present a facial authentication system which employs several cutting-edge techniques. We tested our proposed system on several face databases, both with and without the BioCapsule scheme being embedded into our system. By comparing our results, we quantify the effects the BioCapsule scheme, and its security benefits, have on the accuracy of our facial authentication system.
In this paper, we propose a novel, privacypreserving, and integrated authentication and authorization scheme (dubbed as AuthN-AuthZ). The proposed scheme can address both the usability and privacy issues often posed by authentication through use of privacy-preserving Biometric-Capsule-based authentication. Each Biometric-Capsule encapsulates a user's biometric template as well as their role within a hierarchical Role-based Access Control model. As a result, AuthN-AuthZ provides novel efficiency by performing both authentication and authorization simultaneously in a single operation. To the best of our knowledge, our scheme's integrated AuthN-AuthZ operation is the first of its kind. The proposed scheme is flexible in design and allows for the secure use of robust deep learning techniques, such as the recently proposed and current state-of-the-art facial feature representation method, ArcFace. We conduct extensive experiments to demonstrate the robust performance of the proposed scheme and its AuthN-AuthZ operation.
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