Maintaining physical distance is a crucial aspect of social interactions in the real world. This paper investigates how people embodied in self-avatars, manage such spacing in immersive virtual environments. Specifically, we measured how interpersonal distance (IPD) and peripersonal space (PPS) were affected by a surrounding virtual crowd. We designed three crowd density conditions - no crowd, a medium crowd, and a dense crowd - where participants interacted with a virtual human. Additionally, based on the results of a questionnaire, participants were grouped into one of three groups depending on their self-reported comfort level with crowds: some found crowds relaxing, some were indifferent, and some found crowds stressful. Our findings revealed that IPD decreased as crowd density increased, regardless of the participants' groupings. However, PPS exhibited a more nuanced response, with those stressed by a crowd showing heightened sensitivity to crowd density, reflected by larger PPS boundaries, faster reaction times, and less confidence when estimating their PPS boundary. The study also identified significant interactions between crowd density and participants' comfort levels, particularly among the group stressed by a crowd, where heightened stress led to expanded personal space. These results show the critical role of social and environmental factors in shaping spatial perception and behavior in virtual environments.

Mobile applications can access both sensitive personal data and the network, giving rise to threats of data leaks. App auditing is a fundamental program analysis task to reveal such leaks. Currently, static analysis is the de facto technique which exhaustively examines all data flows and pinpoints problematic ones. However, static analysis generates false alarms for being over-estimated and requires minutes or even hours to examine a real app. These shortcomings greatly limit the usability of automatic app auditing. To overcome these limitations, we design AppAudit that relies on the synergy of static and dynamic analysis to provide effective real-time app auditing. AppAudit embodies a novel dynamic analysis that can simulate the execution of part of the program and perform customized checks at each program state. AppAudit utilizes this to prune false positives of an efficient but over-estimating static analysis. Overall, AppAudit makes app auditing useful for app market operators, app developers and mobile end users, to reveal data leaks effectively and efficiently. We apply AppAudit to more than 1,000 known malware and 400 real apps from various markets. Overall, AppAudit reports comparative number of true data leaks and eliminates all false positives, while being 8.3x faster and using 90% less memory compared to existing approaches. AppAudit also uncovers 30 data leaks in real apps. Our further study reveals the common patterns behind these leaks: 1) most leaks are caused by 3rd-party advertising modules; 2) most data are leaked with simple unencrypted HTTP requests. We believe AppAudit serves as an effective tool to identify data-leaking apps and provides implications to design promising runtime techniques against data leaks.