Leveraging Movement, Posture, and Anthropometric Contexts to Strengthen the Security of Mobile Biometrics
Active authentication is emerging as a promising way to continuously and unobtrusively authenticate smartphone users post-login. Although research in this area has shown that behavioral traits, such as touchscreen gestures and device movements, can be used to distinguish a legitimate user from an attacker, fundamental questions about these traits still remain unanswered. These include: how, and to what extent, do posture and movement impact behavioral traits; what is the impact of human variability (anthropometric properties, age, gender, and health conditions) on behavioral traits; to what extent can these traits be spoofed using posture and movement observations; and how can we strengthen these traits against spoofing attacks. In this project, an interdisciplinary team of investigators from the Computer Science, Biomedical Sciences, Physical Therapy, and Art and Media Technologies at NYIT will leverage capabilities in 3D motion capture, behavioral biometric authentication research, and motor control research to address these questions. This project is funded by the National Science Foundation.
Common smartphone authentication mechanisms such as PINs, graphical passwords, and fingerprint scans offer limited security. They are relatively easy to guess or spoof, and are ineffective when the smartphone is captured after the user has logged in. Multi-modal active authentication addresses these challenges by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interaction, hand movements, gait, voice, and phone location. However, these techniques raise significant privacy and security concerns because the behavioral signals used for authentication represents personal identifiable data, and often expose private information such as user activity, health, and location. Because smartphones can be easily lost or stolen, it is paramount to protect all sensitive behavioral information collected and processed on these devices. One approach for securing behavioral data is to perform off-device authentication via privacy-preserving protocols. However, our experiments show that the energy required to execute these protocols, implemented using state-of-the-art techniques, is unsustainably high, and leads to very quick depletion of the smartphone’s battery. This research advances the state of the art of privacy-preserving active authentication by devising new techniques that significantly reduce the energy cost of cryptographic authentication protocols on smartphones. Further, this research takes into account signals that indicate that the user has lost possession of the smartphone, in order to trigger user authentication only when necessary. The focus of this project is in sharp contrast with existing techniques and protocols, which have been largely agnostic to energy consumption patterns and to the user1s possession of the smartphone post-authentication. The outcome of this project is a suite of new cryptographic techniques and possession-aware protocols that enable secure energy-efficient active authentication of smartphone users. These cryptographic techniques advance the state of the art of privacy-preserving active authentication by re-shaping individual protocol components to take into account complex energy tradeoffs and network heterogeneity, integral to modern smartphones. Finally, this project will focus on novel techniques to securely offload computation related to active authentication from the smartphone to a (possibly untrusted) cloud, further reducing the energy footprint of authentication. The proposed research will thus make privacy-preserving active authentication practical on smartphones, from both an energy and performance perspective. This project is funded by the National Science Foundation.
Hand Movement, Orientation, and Grasp (HMOG) is a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. In this project, we evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy.