Biblio

Wearable devices can be potentially captured or accessed in an unauthorized manner because of their physical nature. In such cases, they are in white-box attack contexts, where the adversary may have total visibility on the implementation of the built-in cryptosystem, with full control over its execution platform. Dealing with white-box attacks on wearable devices is undoubtedly a challenge. To serve as a countermeasure against threats in such contexts, we propose a lightweight encryption scheme to protect the confidentiality of data against white-box attacks. We constructed the scheme's encryption and decryption algorithms on a substitution-permutation network that consisted of random secret components. Moreover, the encryption algorithm uses random padding that does not need to be correctly decrypted as part of the input. This feature enables non-bijective linear transformations to be used in each encryption round to achieve strong security. The required storage for static data is relatively small and the algorithms perform well on various devices, which indicates that the proposed scheme satisfies the requirements of wearable computing in terms of limited memory and low computational power.

In recent years, biometric techniques (e.g., fingerprint or iris) are increasingly integrated into mobile devices to offer security advantages over traditional practices (e.g., passwords and PINs) due to their ease of use in user authentication. However, existing biometric systems are with controversy: once divulged, they are compromised forever - no one can grow a new fingerprint or iris. This work explores a truly cancelable brain-based biometric system for mobile platforms (e.g., smart headwear). Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy mobile authentication, with an application example of smart headwear. Particularly, we address the following research challenges in mobile biometrics with a theoretical and empirical combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire the distinct brain response and analyze unique features in the mobile platform; (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the proposed solution, we conducted a pilot study and achieved an f -score accuracy of 95.46% and equal error rate (EER) of 2.503%, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear. Furthermore, we perform the cancelability study and the longitudinal study, respectively, to show the effectiveness and usability of our new proposed mobile biometric system. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics for secure mobile authentication.

In this paper, we consider side-channel mechanisms, specifically using smart device ambient light sensors, to capture information about user computing activity. We distinguish keyboard keystrokes using only the ambient light sensor readings from a smart watch worn on the user's non-dominant hand. Additionally, we investigate the feasibility of capturing screen emanations for determining user browser usage patterns. The experimental results expose privacy and security risks, as well as the potential for new mobile user interfaces and applications.

Cameras have become nearly ubiquitous with the rise of smartphones and laptops. New wearable devices, such as Google Glass, focus directly on using live video data to enable augmented reality and contextually enabled services. However, granting applications full access to video data exposes more information than is necessary for their functionality, introducing privacy risks. We propose a privilege-separation architecture for visual recognizer applications that encourages modularization and least privilege–-separating the recognizer logic, sandboxing it to restrict filesystem and network access, and restricting what it can extract from the raw video data. We designed and implemented a prototype that separates the recognizer and application modules and evaluated our architecture on a set of 17 computer-vision applications. Our experiments show that our prototype incurs low overhead for each of these applications, reduces some of the privacy risks associated with these applications, and in some cases can actually increase the performance due to increased parallelism and concurrency.

Smartwatches, with motion sensors, are becoming a common utility for users. With the increasing popularity of practical wearable computers, and in particular smartwatches, the security risks linked with sensors on board these devices have yet to be fully explored. Recent research literature has demonstrated the capability of using a smartphone's own accelerometer and gyroscope to infer tap locations; this paper expands on this work to demonstrate a method for inferring smartphone PINs through the analysis of smartwatch motion sensors. This study determines the feasibility and accuracy of inferring user keystrokes on a smartphone through a smartwatch worn by the user. Specifically, we show that with malware accessing only the smartwatch's motion sensors, it is possible to recognize user activity and specific numeric keypad entries. In a controlled scenario, we achieve results no less than 41% and up to 92% accurate for PIN prediction within 5 guesses.

This paper explores experiences with ring and bracelet activity tracker form factors. During the first week of a 2-week field study participants (n=6) wore non-functional mock-ups of ring and bracelet wellness trackers, and provided feedback on their experiences. During the second week, participants used a commercial wellness tracking ring, which collected physical exercise and sleep data and visualized it in a mobile application. Our salient findings based on 196 user diary entries suggest, that the ring form factor is considered beautiful, aesthetic and contributing to the wearer's image. However, the bracelet form factor is more practical for active lifestyle, and preferred in situations where the hands are performing tasks requiring gripping objects, such as sport activities, cleaning the car, cooking and washing dishes. Users strongly identified the ring form factor as jewellery that is intended to be seen, whereas bracelets were considered hidden and inconspicuous elements of the user's ensemble.