Today's hyper-connected consumers demand convenient ways to tune into information without switching between devices, which led the industry leaders to the wearables. Wearables such as smartwatches, fitness trackers, and augmented reality (AR) glasses can be comfortably worn on the body. In addition, they offer limitless features, including activity tracking, authentication, navigation, and entertainment. Wearables that provide digestible information stimulate even higher consumer demand. However, to keep up with the ever-growing user expectations, developers keep adding new features and interaction methods to augment the use cases without considering their privacy impacts. In this dissertation, we explore the privacy dimension of wearables through inference attacks facilitated by machine learning approaches. We start our investigation by exploring the attack surface introduced by fitness trackers. We propose an inference attack that breaches location privacy through the elevation profiles collected by fitness trackers. Our attack highlights that adversaries can infer the location from elevation profiles collected via fitness trackers. Second, we investigated the attack surface introduced by the smartwatches. We introduce an inference attack that exploits the smartwatch microphone to capture the acoustic emanations of physical keyboards and successfully infers what the user has been typing. With this attack, we showed that smartwatches add yet another privacy dimension to be considered. Third, we examined the privacy of AR domain. We designed an inference attack exploiting the geometric projection of hand movements in air. The attack framework predicts the typed text on an in-air tapping keyboard, which is only visible to the user. Our studies uncover various attack surfaces introduced by wearables that have not been studied in literature before. For each attack, we propose possible countermeasures to diminish the ramifications of the risks. We hope that our findings shed light to the privacy risks of wearables and guide the research community to more aware solutions.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Meteriz Yildiran, Ulku, "Exploring the Privacy Dimension of Wearables Through Machine Learning-Enabled Inference" (2022). Electronic Theses and Dissertations, 2020-. 1053.