Please note: This PhD defence will take place online.
Jiayi Chen, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Urs Hengartner
Modern mobile devices allow users to access various applications and services anywhere. However, high mobility also exposes mobile devices to device loss, unauthorized access, and many other risks. Existing studies have proposed a variety of explicit authentication (EA) and implicit authentication (IA) mechanisms to secure sensitive personal and corporate data on mobile devices. Considering the limitations of these mechanisms under different circumstances, we expect that future authentication systems will be able to dynamically determine when and how to authenticate users based on the current context, which is called adaptive authentication. This thesis investigates adaptive authentication from the perspectives of context sensing techniques, authentication and access control adaptations, and adaptation modeling.
First, we investigate the smartphone loss scenario. Context sensing is critical for triggering immediate device locking with re-authentication and an alert to the owner before they leave without the phone. We propose Chaperone, an active acoustic sensing based solution to detect a user’s departure from the device. It is designed to robustly provide a user’s proximity and motion contexts in real-world scenarios characterized by bursting high-frequency noise, bustling crowds, and diverse environmental layouts. Extensive evaluations at a variety of real-world locations have shown that Chaperone has high accuracy and low detection latency under various conditions.
Second, we investigate temporary device sharing as a special scenario of adaptive authentication. We propose device sharing awareness (DSA), a new sharing-protection approach for temporarily shared mobile devices. DSA exploits natural handover gestures and behavioral biometrics as contextual factors to transparently enable and disable a device’s sharing mode without requiring explicit input of the device owner. It also supports various access control strategies to fulfill sharing requirements imposed by an app. Our user study has shown the effectiveness of handover detection and demonstrated how DSA automatically processes sharing events to provide a secure sharing environment.
Third, we investigate the adaptation of an IA system to shared mobile devices to reject imposters and distinguish between legitimate users in real-time. We propose a multi-user IA solution that incorporates multiple modalities and supports adding new users and automatically labeling new incoming data for model updating. Our solution adopts a score fusion strategy based on Dempster-Shafer (D-S) theory to improve accuracy with considering uncertainties among different IA mechanisms. We also provide an evaluation framework to support IA researchers in the evaluation of multi-user, multi-modal IA systems. We present two sample use cases to showcase how our framework helps address practical design questions of multi-user IA systems.
Fourth, we investigate a high-level organization of different adaptation policies in an adaptive authentication system. We design and build a multi-stage risk-aware adaptive authentication and access control framework (MRAAC). MRAAC organizes adaptation policies in multiple stages to handle various scenarios and progressively adapts authentication mechanisms based on context, resource sensitivity, and user authenticity. We present three use cases to show how MRAAC enables various stakeholders (device manufacturers, enterprise and secure app developers) to provide adaptive authentication workflows on COTS Android with low processing and battery overhead.
In conclusion, this thesis fills the gaps in adaptive authentication systems for shared mobile devices and adaptation models for authentication and access control. Our frameworks and implementations also benefit researchers and developers to develop and evaluate their adaptive authentication systems efficiently.
To join this PhD defence on Zoom, please go to https://uwaterloo.zoom.us/j/92140309729.
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