Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

As biometric templates consist of highly correlated features, the real security level offered by biometric authentication systems remains an open research question. In this work we provide new approximations and a lower bound of the security offered by fuzzy commitment schemes. Fuzzy commitment cryptosystems and in general biometric template protection schemes play an important role in allowing for remote storage and processing of biometric data, as they mitigate the threat of biometric template leakage. The use of such schemes would alleviate some of the usability constraints imposed by the state-of-practice local use of biometrics. As such we conduct an in-depth security analysis for IMU based gait authentication systems, where we evaluate the effectiveness of attacks within the scope of two well-defined threat models that target both unprotected and protected systems. A pivotal enabler of our analysis is the development of nine different approaches to gait authentication, which allows us to perform intramodal fusion on these distinct, yet highly correlated biometric templates, and to protect them with a strengthened fuzzy commitment scheme. Our analysis clearly demonstrates the high correlation between the different biometric templates, which, among others, further showcases the threat of biometric template leakage. Furthermore, as our analysis incorporates a threat model that assumes biometric template leakage, it provides metrics for the security provided by the biometric modality itself.

The current body of work on adversarial robustness seems to imply that theoretical robustness against adversarial examples leads to more secure systems. In this paper we demonstrate that this premise is erroneous by assessing the strengths and limitations of prominent robustness methods in light of facial authentication, a realistic use case where adversarial perturbations pose a real threat, which allows for a natural reflection on the security gained by the obtained robustness. The main contribution of this paper is an evaluation and critical reflection upon why prominent robustness methods fail to deliver a secure system despite living up to their promises of adding robustness. Our analysis shows that state-of-the-art robustness methods such as Adversarial Training and Guided Complement Entropy struggle to accommodate for two key requirements of facial authentication: (1)~the threat model of facial authentication assumes physical adversarial examples that can be added to the scene as opposed to "classical" adversarial examples that are applied to the digital input. Moreover, an attacker that can directly perturb digital input, does not require adversarial perturbations to impersonate their victim; (2)~robustness properties are only validated for standard classification problems, and often ignore the impact of more practical training paradigms that re-purpose models. Our extensive evaluation of robustness in light of facial authentication allowed us to pinpoint the limitations of these methods. To ensure that the concepts of adversarial robustness and security are more tightly coupled, we recommend to evaluate new defences with applications where adversarial perturbations pose a security threat.

Behaviometrics in multi-factor authentication schemes continuously assess behavior patterns of a subject to recognize and verify his identity. In this work we challenge the practical feasibility and the resilience of accelerometer-based gait analysis as a behaviometric under sensor displacement conditions. To improve misauthentication resistance, we present and evaluate a solution using multiple accelerometers on 7 positions on the body during different activities and compare the effectiveness with Gradient-Boosted Trees classification. From a security point of view, we investigate the feasibility of zero and non-zero effort attacks on gait analysis as a behaviometric. Our experimental results with data from 12 individuals show an improvement in terms of EER with about 2% (from 5% down to 3%), with an increased resilience against observation attacks. When trained to defend against such attacks, we observe no decrease in classification performance.

We propose, design, and evaluate PIVOT, a privacy-enhancing and effective contact tracing solution that aims to strike a balance between utility and privacy: one that does not collect sensitive information yet allowing effective tracing and notifying the close contacts of diagnosed users. PIVOT requires a considerably low degree of trust in the entities involved compared to centralised alternatives while retaining the necessary utility. To protect users’ privacy, it uses local proximity tracing based on broadcasting and recording constantly changing anonymous public keys via short-range communication. These public keys are used to establish a shared secret key between two people in close contact. The three keys (i.e., the two public keys and the established shared key) are then used to generate two unique per-user-per-contact hashes: one for infection registration and one for exposure score query. These hashes are never revealed to the public. To improve utility, user exposure score computation is performed centrally, which provides health authorities with minimal, yet insightful and actionable data. Data minimisation is achieved by the use of per-user-per-contact hashes and by enforcing role separation: the health authority act as a mixing node, while the matching between reported and queried hashes is outsourced to a third entity, an independent matching service. This separation ensures that out-of-scope information, such as users’ social interactions, is hidden from the health authorities, whereas the matching service does not learn users’ sensitive information. To sustain our claims, we conduct a practical evaluation that encompasses anonymity guarantees and energy requirements.