Increasingly, many security and privacy sensitive applications (apps for short) are running in the mobile platforms. However, as the mobile operating systems are becoming increasingly sophisticated, they are vulnerable to various attacks. In addressing the need of running high assurance mobile apps in a secure environment even though the operating systems are untrusted, this paper presents VTRUST, a new mobile app trusted execution environment, which offloads the general execution and storage of a mobile app to a trusted remote server (e.g., a VM running in a cloud) and secures the I/O between the server and the mobile device with the aid of a trusted hypervisor on the mobile device. Specifically, VTRUST establishes an encrypted I/O channel between the local hypervisor and the remote server, such that any sensitive data flowing through the mobile OS, which is hosted by the hypervisor, is encrypted from the perspective of the local mobile OS. To enhance the performance of VTRUST, we have also designed multiple optimizations, such as output data compression and selective sensor data transmission. We have implemented VTRUST and our evaluation shows that it has limited impact on both user experience and the app performance.

The rise of edge computing gives birth to a spectrum of delay-sensitive applications. Many of these applications build their services atop the functionality that the edge nodes quickly negotiate a unique order on the events received from a massive number of client devices, even under very high event rates. To this end, we propose a protocol, called Nomad, for achieving fast event ordering in edge computing environments. Nomad is designed as a consensus protocol that employs a lease-based approach to take advantage of the locality of the unbalanced workload across the system. It also dynamically adjusts the leadership distribution on the edge nodes based on the recent running history, and relies on a cloud-based arbitrator to resolve contentions. Experiments demonstrate that Nomad outperforms the existing solutions, such as Multi-Paxos, Mencius and E-Paxos, in achieving fast event ordering for large-scale, delay-sensitive edge-cloud applications.

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing.

Mobile users across edge networks require seamless migration of offloading services. Edge computing platforms must smoothly support these service transfers and keep pace with user movements around the network. However, live migration of offloading services in the wide area network poses significant service handoff challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture which supports seamless migration of offloading services while also keeping the moving mobile user “in service” with its nearest edge server. We identify a critical problem in the state-of-the-art tool for Docker container migration. Based on our systematic study of the Docker container storage system, we propose to leverage the layered nature of the storage system to reduce file system synchronization overhead, without dependence on the distributed file system. In contrast to the state-of-the-art service handoff method in the edge environment, our system yields a 80 percent (56 percent) reduction in handoff time under 5 Mbps (20 Mbps) network bandwidth conditions.

Because of the smaller size of mobile devices, text entry with on-screen keyboards becomes inefficient. Therefore, we present CamK, a camera-based text-entry method, which can use a panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. With the built-in camera of the mobile device, CamK captures images during the typing process and utilizes image processing techniques to recognize the typing behavior, i.e., extract the keys, track the user's fingertips, detect, and locate keystrokes. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. To reduce the time latency, CamK optimizes computation-intensive modules by changing image sizes, focusing on target areas, introducing multiple threads, removing the operations of writing or reading images. Finally, we implement CamK on mobile devices running Android. Our experimental results show that CamK can achieve above 95 percent accuracy in keystroke localization, with only a 4.8 percent false positive rate. When compared with on-screen keyboards, CamK can achieve a 1.25X typing speedup for regular text input and 2.5X for random character input. In addition, we introduce word prediction to further improve the input speed for regular text by 13.4 percent.

We present LAVEA, a system built for edge computing, which offloads computation tasks between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-edge collaboration. Our results reveal that the client-edge configuration has task speedup against local or client-cloud configurations.

Smartphone lock screens are implemented to reduce the risk of data loss or compromise given the fact that increasing amount of person data are accessible on smartphones nowadays. Unfortunately, many smartphone users abandon lock screens due to the inconvenience of unlocking their phones many times a day. With the wide adoption of wearables, token-based approaches have gained popularity in simplifying unlocking and retaining security at the same time. To this end, we propose to take advantage of the smartwatch for easy smartphone unlocking. In this paper, we have designed WearLock, a system that uses acoustic tones as tokens to automate the unlocking securely. We build a sub-channel selection and an adaptive modulation in the acoustic modem to maximize unlocking success rate against ambient noise only when those two devices are nearby. We leverage the motion sensor on the smartwatch to reduce the unlock frequency. We offload smartwatch tasks to the smartphone to speed up computation and save energy. We have implemented the WearLock prototype and conducted extensive evaluations. Results achieved a low average bit error rate (BER) as 8% in various experiments. Compared to traditional manual personal identification numbers (PINs) entry, WearLock achieves at least 18% unlock speedup without any manual effort.

Smartphone users are often grouped to exchange files or perform collaborative tasks when meeting together. We argue that the location information of group members is critical to many mobile applications. Existing localization solutions mostly rely on anchor nodes or infrastructures to perform ranging and positioning. These approaches are inefficient for ad hoc scenarios. In this paper, we propose AMIL, an Acoustic Mobility-Induced TDoA (Time-Difference-of-Arrival)-based Localization scheme for smartphones. In AMIL, a smartphone user can use simple gestures (e.g., hold the phone and draw a triangle in the air) to quickly obtain the relative coordinates of neighboring mobile devices. We have implemented and evaluated AMIL on off-the-shelf smartphones. The field tests have shown that our scheme can achieve less than three degree orientation errors and can successfully build a simple map of 12 people in an office room with average error of 50cm.

We have seen an emerging trend towards wearables nowadays. In this paper, we focus on smart glasses, whose current interfaces are difficult to use, error-prone, and provide no or insecure user authentication. We thus present GlassGesture, a system that improves Google Glass through a gesture-based user interface, which provides efficient gesture recognition and robust authentication. First, our gesture recognition enables the use of simple head gestures as input. It is accurate in various wearer activities regardless of noise. Particularly, we improve the recognition efficiency significantly by employing a novel similarity search scheme. Second, our gesture-based authentication can identify owner through features extracted from head movements. We improve the authentication performance by proposing new features based on peak analyses, and employing an ensemble method. Last, we implement GlassGesture and present extensive evaluations. GlassGesture achieves a gesture recognition accuracy near 96%. For authentication, GlassGesture can accept authorized users in near 92% of trials, and reject attackers in near 99% of trials. We also show that in 100 trials imitators cannot successfully masquerade as the authorized user even once.

Due to the smaller size of mobile devices, on-screen keyboards become inefficient for text entry. In this paper, we present CamK, a camera-based text-entry method, which uses an arbitrary panel (e.g., a piece of paper) with a keyboard layout to input text into small devices. CamK captures the images during the typing process and uses the image processing technique to recognize the typing behavior. The principle of CamK is to extract the keys, track the user's fingertips, detect and localize the keystroke. To achieve high accuracy of keystroke localization and low false positive rate of keystroke detection, CamK introduces the initial training and online calibration. Additionally, CamK optimizes computation-intensive modules to reduce the time latency. We implement CamK on a mobile device running Android. Our experiment results show that CamK can achieve above 95% accuracy of keystroke localization, with only 4.8% false positive keystrokes. When compared to on-screen keyboards, CamK can achieve 1.25X typing speedup for regular text input and 2.5X for random character input.

Cognitive radio plays an important role in improving spectrum utilization in wireless services. In the cognitive radio paradigm, secondary users (SUs) are allowed to utilize licensed spectrum opportunistically without interfering with primary users (PUs). To motivate PU to share licensed spectrum with SU, it is reasonable for SU to pay PU a fee whenever the former is utilizing the latter's licensed spectrum. SU's detailed usage information, such as when and how long the licensed spectrum is utilized, is needed for PU to calculate payment. Providing usage information to PU, however, may compromise SU's privacy. To solve this dilemma, we are the first to propose a novel privacy-preserving mechanism for cognitive radio transactions through commitment scheme and zero-knowledge proof. This mechanism, on one hand, only allows PU to know the total payment to SU for a billing period, plus a little portion of SU's usage information. On the other hand, it guarantees PU that the payment is correctly calculated. We have implemented our mechanism and evaluated its performance.