The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize the information for activity tracking and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms and allow users to share activities on such platforms or even with other social network platforms. To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning- and deep learning-based techniques and achieve a prediction success rate ranging from 59.59% to 99.80%. The findings are alarming, highlighting that sharing elevation information may have significant location privacy risks.

Enabling users to push the physical world’s limits, augmented and virtual reality platforms opened a new chapter in perception. Novel immersive experiences resulted in the emergence of new interaction methods for virtual environments, which came with unprecedented security and privacy risks. This paper presents a keylogging inference attack to infer user inputs typed with in-air tapping keyboards. We observe that hands follow specific patterns when typing in the air and exploit this observation to carry out our attack. Starting with three plausible attack scenarios where the adversary obtains the hand trace patterns of the victim, we build a pipeline to reconstruct the user input. Our attack pipeline takes the hand traces of the victim as an input and outputs a set of input inferences ordered from the best to worst. Through various experiments, we showed that our inference attack achieves a pinpoint accuracy ranging from 40% to 87% within at most the top-500 candidate reconstructions. Finally, we discuss countermeasures, while the results presented provide a cautionary tale of the security and privacy risk of the immersive mobile technology.

Highly-configurable software underpins much of our computing infrastructure. It enables extensive reuse, but opens the door to broken configuration specifications. The configuration specification language, Kconfig, is designed to prevent invalid configurations of the Linux kernel from being built. However, the astronomical size of the configuration space for Linux makes finding specification bugs difficult by hand or with random testing. In this paper, we introduce a software model checking framework for building Kconfig static analysis tools. We develop a formal semantics of the Kconfig language and implement the semantics in a symbolic evaluator called kclause that models Kconfig behavior as logical formulas. We then design and implement a bug finder, called kismet, that takes kclause models and leverages automated theorem proving to find unmet dependency bugs. kismet is evaluated for its precision, performance, and impact on kernel development for a recent version of Linux, which has over 140,000 lines of Kconfig across 28 architecture-specific specifications. Our evaluation finds 781 bugs (151 when considering sharing among Kconfig specifications) with 100% precision, spending between 37 and 90 minutes for each Kconfig specification, although it misses some bugs due to underapproximation. Compared to random testing, kismet finds substantially more true positive bugs in a fraction of the time.

The extensive use of smartphones and wearable devices has facilitated many useful applications. For example, with Global Positioning System (GPS)-equipped smart and wearable devices, many applications can gather, process, and share rich metadata, such as geolocation, trajectories, elevation, and time. For example, fitness applications, such as Runkeeper and Strava, utilize information for activity tracking, and have recently witnessed a boom in popularity. Those fitness tracker applications have their own web platforms, and allow users to share activities on such platforms, or even with other social network platforms. To preserve privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users. In this work, and as a cautionary tale, we create a proof of concept where we examine the extent to which elevation profiles can be used to predict the location of users. To tackle this problem, we devise three plausible threat settings under which the city or borough of the targets can be predicted. Those threat settings define the amount of information available to the adversary to launch the prediction attacks. Establishing that simple features of elevation profiles, e.g., spectral features, are insufficient, we devise both natural language processing (NLP)-inspired text-like representation and computer vision-inspired image-like representation of elevation profiles, and we convert the problem at hand into text and image classification problem. We use both traditional machine learning-and deep learning-based techniques, and achieve a prediction success rate ranging from 59.59% to 95.83%. The findings are alarming, and highlight that sharing elevation information may have significant location privacy risks.