Real-time mobile applications such as AR/VR, cloud gaming, and collaborative robotics rely on edge computing to maintain ultra-low latency, yet they can suffer noticeable service interruptions when users move because the application's session state must migrate to a new edge site. We present EdgeWarp, a system that delivers instant mobility for stateful edge applications over 5G while laying the groundwork for 6G. EdgeWarp employs a two-step synchronization protocol that proactively mirrors application state at edge sites predicted to serve the user next, sharply reducing transfer delays, and it signals each session's latency budget to the 5G control plane so that critical flows are prioritized during handover. Experiments with real applications and 4G/5G radio traces show that EdgeWarp cuts mobility-induced downtime by up to 15.4×, charting a path toward zero-downtime edge computing—a prerequisite for emerging 6G scenarios such as holographic telepresence, tactile Internet, and large-scale digital twins. We have made our anonymized code publicly accessible here.

Extended Reality (XR) headsets are increasingly integrating eye tracking for enhanced user experience, adaptive interfaces, and foveated rendering. However, this rich biometric signal introduces new privacy risks. In this work, we demonstrate that eye-tracking data collected by commercial XR devices can be exploited to infer sensitive user activity. We leverage users' gaze sequences captured while interacting with a VR app to classify the type of content a user is watching. Our results reveal that eye movements alone, without any video or audio context, carry enough information to accurately predict content categories. We discuss the implications of this threat and outline how eye tracking can potentially be used to fingerprint applications and user behavior. This work is a step towards exposing and mitigating emerging privacy threats in immersive systems.

The widespread adoption of conversational AI platforms has introduced new security and privacy risks. While these risks and their mitigation strategies have been extensively researched from a technical perspective, users' perceptions of these platforms' security and privacy remain largely unexplored. In this paper, we conduct a large-scale analysis of over 2.5M user posts from the r/ChatGPT Reddit community to understand users' security and privacy concerns and attitudes toward conversational AI platforms. Our qualitative analysis reveals that users are concerned about each stage of the data lifecycle (i.e., collection, usage, and retention). They seek mitigations for security vulnerabilities, compliance with privacy regulations, and greater transparency and control in data handling. We also find that users exhibit varied behaviors and preferences when interacting with these platforms. Some users proactively safeguard their data and adjust privacy settings, while others prioritize convenience over privacy risks, dismissing privacy concerns in favor of benefits, or feel resigned to inevitable data sharing. Through qualitative content and regression analysis, we discover that users' concerns evolve over time with the evolving AI landscape and are influenced by technological developments and major events. Based on our findings, we provide recommendations for users, platforms, enterprises, and policymakers to enhance transparency, improve data controls, and increase user trust and adoption.