Phillips, TylerYu, XiaoyuanHaakenson, BrandonGoyal, ShreyaZou, XukaiPurkayastha, SaptarshiWu, Huanmei2022-10-062022-10-062020-10Phillips, T., Yu, X., Haakenson, B., Goyal, S., Zou, X., Purkayastha, S., & Wu, H. (2020, October). AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization. In 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) (pp. 189-198). IEEE. https://doi.org/10.1109/TPS-ISA50397.2020.00034https://hdl.handle.net/1805/30223In this paper, we propose a novel, privacy-preserving, 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.enPublisher Policyauthenticationface recognitiondeep learningConference proceedings