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The immersive nature of Virtual Reality (VR) and its reliance on sensory devices like head-mounted displays introduce privacy risks to users. While earlier research has explored users' privacy concerns within VR environments, less is known about users' comprehension of VR data practices and protective behaviors; the expanding VR market and technological progress also necessitate a fresh evaluation. We conducted semi-structured interviews with 20 VR users, showing their diverse perceptions regarding the types of data collected and their intended purposes. We observed privacy concerns in three dimensions: institutional, social, and device-specific. Our participants sought to protect their privacy through considerations when selecting the device, scrutinizing VR apps, and selective engagement in different VR interactions. We contrast our findings with observations from other technologies and ecosystems, shedding light on how VR has altered the privacy landscape for end-users. We further offer recommendations to alleviate users' privacy concerns, rectify misunderstandings, and encourage the adoption of privacy-conscious behaviors. 

Smart home devices are constantly exchanging data with a variety of remote endpoints. This data encompasses diverse information, from device operation and status to sensitive user information like behavioral usage patterns. However, there is a lack of transparency regarding where such data goes and with whom it is potentially shared. This paper investigates the diverse endpoints that smart home Internet-of-Things (IoT) devices contact to better understand and reason about the IoT backend infrastructure, thereby providing insights into potential data privacy risks. We analyze data from 5,413 users and 25,123 IoT devices using the IoT Inspector, an open-source application allowing users to monitor traffic from smart home devices on their networks. First, we develop semi-automated techniques to map remote endpoints to organizations and their business types to shed light on their potential relationships with IoT end products. We discover that IoT devices contact more third or support-party domains than first-party domains. We also see that the distribution of contacted endpoints varies based on the user's location and across vendors manufacturing similar functional devices, where some devices are more exposed to third parties than others. Our analysis also reveals the major organizations providing backend support for IoT smart devices and provides insights into the temporal evolution of cross-border data-sharing practices. 

Despite the significant benefits of the widespread adoption of smart home Internet of Things (IoT) devices, these devices are known to be vulnerable to active and passive attacks. Existing literature has demonstrated the ability to infer the activities of these devices by analyzing their network traffic. In this study, we introduce a packet-based signature generation and detection system that can identify specific events associated with IoT devices by extracting simple features from raw encrypted network traffic. Unlike existing techniques that depend on specific time windows, our approach automatically determines the optimal number of packets to generate unique signatures, making it more resilient to network jitters. We evaluate the effectiveness, uniqueness, and correctness of our signatures by training and testing our system using four public datasets and an emulated dataset with varying network delays, verifying known signatures and discovering new ones. Our system achieved an average recall and precision of 98-99% and 98-100%, respectively, demonstrating the effectiveness and feasibility of using packet-level signatures to detect IoT device activities. 

Voice assistants are becoming increasingly pervasive due to the convenience and automation they provide through the voice interface. However, such convenience often comes with unforeseen security and privacy risks. For example, encrypted traffic from voice assistants can leak sensitive information about their users' habits and lifestyles. In this paper, we present a taxonomy of fingerprinting voice commands on the most popular voice assistant platforms (Google, Alexa, and Siri). We also provide a deeper understanding of the feasibility of fingerprinting third-party applications and streaming services over the voice interface. Our analysis not only improves the state-of-the-art technique but also studies a more realistic setup for fingerprinting voice activities over encrypted traffic.Our proposed technique considers a passive network eavesdropper observing encrypted traffic from various devices within a home and, therefore, first detects the invocation/activation of voice assistants followed by what specific voice command is issued. Using an end-to-end system design, we show that it is possible to detect when a voice assistant is activated with 99% accuracy and then utilize the subsequent traffic pattern to infer more fine-grained user activities with around 77-80% accuracy. 

Online trackers are invasive as they track our digital footprints, many of which are sensitive in nature, and when aggregated over time, they can help infer intricate details about our lifestyles and habits. Although much research has been conducted to understand the effectiveness of existing countermeasures for the desktop platform, little is known about how mobile browsers have evolved to handle online trackers. With mobile devices now generating more web traffic than their desktop counterparts, we fill this research gap through a large-scale comparative analysis of mobile web browsers. We crawl 10K valid websites from the Tranco list on real mobile devices. Our data collection process covers both popular generic browsers (e.g., Chrome, Firefox, and Safari) as well as privacy-focused browsers (e.g., Brave, Duck Duck Go, and Firefox-Focus). We use dynamic analysis of runtime execution traces and static analysis of source codes to highlight the tracking behavior of invasive fingerprinters. We also find evidence of tailored content being served to different browsers. In particular, we note that Firefox Focus sees altered script code, whereas Brave and Duck Duck Go have highly similar content. To test the privacy protection of browsers, we measure the responses of each browser in blocking trackers and advertisers and note the strengths and weaknesses of privacy browsers. To establish ground truth, we use well-known block lists, including EasyList, EasyPrivacy, Disconnect and WhoTracksMe and find that Brave generally blocks the highest number of content that should be blocked as per these lists. Focus performs better against social trackers, and Duck Duck Go restricts third-party trackers that perform email-based tracking.