OS-level virtualization (e.g., Linux containers) has become a cornerstone of modern cloud systems. While it offers the illusion of isolated kernels for processes, these processes share the same underlying kernel, raising critical concerns around security, fault isolation, and the inability to customize kernels. Existing solutions address the issues by employing virtual machines that isolate kernels. However, these approaches incur significant performance overhead.

Use-after-free (UAF) vulnerabilities pose severe security risks in memory-unsafe languages like C and C++. To mitigate these issues, prior work has employed memory sweeping, inspired by conservative garbage collection. However, such approaches inherit key limitations, including stop-the-world pauses, poor scalability, and high CPU usage, rendering them unsuitable for modern, latency-sensitive applications. This paper presents SwiftSweeper, a secure memory allocator designed to prevent UAF vulnerabilities in unmodified binaries. SwiftSweeper reimagines memory sweeping by eliminating stop-the-world pauses and enhancing scalability to support high-performance C and C++ workloads. It features an efficient and secure in-kernel data path, implemented using eBPF (XMP, eXpress Memory Path), and a co-designed user-level allocator and kernel. We implement SwiftSweeper on Linux and demonstrate that it delivers state-of-the-art performance, memory efficiency, and minimal latency overhead across both single-threaded and multi-threaded applications, including SPEC CPU and WebServer benchmarks.

In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key challenges persist for fine-grained localization: i) devices must generate sufficient, consistent uplink traffic over time, ii) target devices may transmit uplink signals at very low power, and iii) signals from cellular repeaters may hinder localization of the target device. While these challenges pose significant practical obstacles to localization, they have been largely overlooked in existing research.

LTE sniffers are important for security and performance analysis because they can passively capture the wireless traffic of users in LTE network. However, existing open-source LTE sniffers have only limited functionality and cannot decode data traffic. This paper introduces LTESNIFFER, the first open-source LTE sniffer that can passively decode both uplink and downlink data traffic. Implementing a sniffer is not trivial because one needs to understand detailed configurations and parameters to successfully decode each user's traffic. Using multiple techniques, we found mechanisms to understand these, which improves our decoding performance. We evaluated the performance of LTESNIFFER on both testbed and commercial network environments. We also compare the performance of LTESNIFFER with AirScope, a popular commercial LTE sniffer. Additionally, LTESNIFFER provides a proof-of-concept API with three functions that can be used for security applications, including identity mapping, identity collecting, and device capability profiling. We release LTESNIFFER as open-source for future research.

Augmented reality (AR) applications have recently become popular on modern smartphones. We explore the effectiveness of this mobile AR technology in the context of grocery shopping, in particular as a means to assist shoppers in making healthier decisions as they decide which grocery products to buy. We construct an AR-assisted mobile grocery-shopping application that makes real-time, customized recommendations of healthy products to users and also highlights products to avoid for various types of health concerns, such as allergies to milk or nut products, low-sodium or low-fat diets, and general caloric intake. We have implemented a prototype of this AR-assisted mobile grocery shopping application and evaluated its effectiveness in grocery store aisles. Our application's evaluation with typical grocery shoppers demonstrates that AR overlay tagging of products reduces the search time to find healthy food items, and that coloring the tags helps to improve the user's ability to quickly and easily identify recommended products, as well as products to avoid. We have evaluated our application's functionality by analyzing the data we collected from 15 in-person actual grocery-shopping subjects and 104 online application survey participants.

Online video chat services such as Chatroulette and Omegle randomly match users in video chat sessions and have become increasingly popular, with tens of thousands of users online at anytime during a day. Our interest is in examining user behavior in the growing domain of mobile video, and in particular how users behave in such video chat services as they are extended onto mobile clients. To date, over four thousand people have downloaded and used our Android-based mobile client, which was developed to be compatible with an existing video chat service. The paper provides a first-ever detailed large scale study of mobile user behavior in a random video chat service over a three week period. This study identifies major characteristics such as mobile user session durations, time of use, demographic distribution and the large number of brief sessions that users click through to find good matches. Through content analysis of video and audio, as well as analysis of texting and clicking behavior, we discover key correlations among these characteristics, e.g., normal mobile users are highly correlated with using the front camera and with the presence of a face, whereas misbehaving mobile users have a high negative correlation with the presence of a face.

Online video chat services such as Chatroulette [1] and Omegle [2] that randomly match pairs of users in video chat sessions have become increasingly popular, with over twenty thousand online users at anytime during a day. A key problem encountered in such systems is the presence of misbehaving users ("flashers") and obscene content. Our previous works [3] [4] prove that using some image recognition methods (skin-detection, dense SIFT) and machine learning algorithms could achieve significantly higher recall and better precision for flasher detection. Nowadays, with the rapid development of advanced mobile phones with both front and back cameras, we expect mobile video chat to become a popular extension of online video chat services. However, because of the computation-intensive features used by our previous solutions and mobile phones' hardware limitations such as memory size and CPU capacity, it is difficult to directly apply our previous works to mobile platforms. As smartphones are increasingly equipped with diverse sensing capabilities, we plan to utilize this multi-dimensional sensor information to extend flasher detection on mobile platform. This project explores how we can mine accelerometer and other mobile sensor data to infer some clues to optimize flasher detection accuracy while reducing the computation demands of flasher detection on the mobile device.

Emergency applications have recently become widely available on modern smart phones. Nearly all of these commercial applications have focused on providing simple accident information in outdoor settings. AR in indoor environments poses unique challenges, due to the unavailability of GPS indoors and WiFi-based positioning limitations. In this paper, we propose the use of Rescue Me, a novel system based on indoor mobile AR applications using personalized pedometry and one that recommends the most optimal, uncrowded exit path to users. We have developed the Rescue Me application for use within large scale buildings, with complex paths. We show how Rescue Meleverages the sensors on a smart phone, in conjunction with emergency information and daily-based user behavior, to deliver evacuation information in emergency situations.

Recently the demand on high graphic ability has increased in handheld systems due to multimedia or game application. Conventional DVS (dynamic voltage scaling) methods focus on reducing energy consumption of CPU. They didn't consider the influence of multimedia accelerators to make DVS policies. From the preliminary experiment, we found that the conventional DVS algorithm is not optimal to a system with a multimedia accelerator in context of reducing energy consumption of the system. We propose a power management mechanism that guarantees both QoS and reduction of energy consumption by considering the relation between the frequencies of the CPU and the frequencies of the multimedia accelerator. The proposed mechanism is a DVS technique in context of multimedia accelerator. We experimented the proposed mechanism on a testbed equipped with Intel XScale processor and Intel 2700 G multimedia accelerator. The experimental result shows that the proposed method reduces energy consumption of the system as much as by 33% compared to conventional CPU-only DVS algorithms.