ScooterLab is a National Science Foundation (NSF) funded community research infrastructure initiative, currently under development at the University of Texas at San Antonio (UTSA). This publicly-available micromobility testbed and crowd-sensing/crowd-sourcing infrastructure will provide researchers access to a community of riders and a fully operational fleet of highly customizable dockless e-scooters. The research team is prototyping infrastructure to be easily retrofitted with state-of-the-art sensors in partnership with collaborators, enableing real-time collection of fine-grained research data from micromobility rides.
This project plans to uncover new security and privacy risks in modern ubiquitous sensing and computing environments comprising of functionally heterogeneous and isolated sensors and applications and evaluate their feasibility in practical settings. To enable secure and privacy-preserving sensor access by applications, the project will design a novel framework that exposes and harnesses contextual information about users both at the device and network levels for achieving fine-grained and user-approved access control. The first design, called ConWare, will harness the exposed user context at the device level by defining user-approved access bindings between these contexts and the set of allowable sensing actions on that device within those contexts. The second design, called ConWareNet, will harness the exposed user context at the communication network level to regulate applications across an autonomous set of devices using similar user-approved bindings or policies. The project will also address the issue of preventing misuse of the exposed context and will design adaptation mechanisms for these frameworks to self-adjust in a dynamic sensing and actuation environment.
The goal of this research project is twofold: first, to demonstrate that wearable devices enable novel side-channel security and privacy threats, and second, to design continuous authentication techniques and adaptive access control mechanisms to survive these threats. Specifically, this research will evaluate private data inference and wearer tracking threats in wearable devices that utilize unprotected sensors as side-channels. This will be accomplished by designing appropriate learning-based classification and prediction mechanisms that can be used by an adversary for inferring sensitive data. On the protection front, this project will develop a multi-sensor activity and identity classification framework. This framework will leverage rich contextual sensor data (e.g., fine-grained movements, application usage and critical body parameters) to enable continuous identification and authentication of legitimate wearers and their activities.
Pedestrian distraction continues to be a significant pedestrian safety concern in urban communities. Existing pedestrian safety frameworks and applications are unable to simultaneously detect complex distraction-related activities (often focusing on detecting only specific activities and contexts) and ignore the hazards posed by the distracted pedestrian to fellow pedestrians and drivers. Moreover, a majority of existing complex activity recognition schemes are either computationally impractical for real-time implementation on mainstream mobile and wearable devices, or employ specialized auxiliary hardware, or both. Thus, there is an urgent need for usable and accurate pedestrian safety solutions that can be efficiently implemented (and used) on commercial off-the- shelf (or COTS) mobile and wearable devices. This project proposes a cloud-assisted pedestrian safety framework that detects several commonly observed activities resulting in pedestrian distraction by using multi-modal and multi-source data from users’ mobile and wearable devices, and provides appropriate on-device and community notifications, with the objective of achieving a favorable balance between responsiveness, computational efficiency, detection accuracy and usability.
Binary code analysis is the process of analyzing compiled binaries or assembly language code, for example, pre-packaged executable software or firmware, without access to the corresponding source code in order to uncover or match specific functional patterns or signatures. Binary code analysis has various high-impact use cases, such as malware detection, (zero-day) vulnerability discovery, plagiarism detection and runtime crash analysis on the user-end. Prior research efforts that have attempted to address the problem of binary code similarity and containment using either static or dynamic analysis have primarily focused on individual Instruction Set Architectures (ISAs). In other words, these efforts consider or analyze binaries (for similarity and containment) that have been compiled on the same ISA. In this project, we propose Ditto, a new binary code similarity and containment analysis framework that can be universally and efficiently applied to binaries of any ISA, including binaries that may have undergone obfuscation and/or optimization.
One significant research gap is a lack of understanding of the strategic behavior of rational processors within committees in shard-based consensus protocols. Such an understanding is critical for designing appropriate incentives that will foster cooperation within committees and prevent free-riding. We analyze the behavior of processors using a game-theoretic model, where each processor aims at maximizing its reward at a minimum cost of participating in the protocol. We first analyze the Nash equilibria in an N-player static game model of the sharding protocol. We show that depending on the reward sharing approach employed, processors can potentially increase their payoff by unilaterally behaving in a defective fashion, thus resulting in a social dilemma. In order to overcome this social dilemma, we propose a novel incentive-compatible reward sharing mechanism to promote cooperation among processors.
The increasing popularity of Online Social Networks (OSNs) is spawning new security and privacy concerns. Currently, a majority of OSNs offer very naive access controls that are primarily based on static Access Control Lists (ACL) or policies. But as the number of social connections grow, a static ACL based approach slowly becomes ineffective and unappealing to OSN users. There is an increased need to control access to data based on the associated context, rather than solely on data ownership and social links. Surveillance by the OSN service provider is another critical concern for OSN users, as the service provider may further scrutinize data posted or shared by users for personal gains (e.g., targeted advertisements), for use by corporate partners or to comply with legal orders. In this project, we introduce a novel paradigm of context-based access control in OSNs, where users (in the sharer’s social network) are able to access the shared data only if they have knowledge of the context associated with it.
Significant developments in the electric power industry are in the areas of advanced measurements, improved communication infrastructure, renewable energy sources, and electric vehicles. These changes are expected to influence the way energy is provided to and consumed by customers. Advanced Metering Infrastructure (AMI) initiatives are a popular tool to incorporate these changes for modernizing the electricity grid, reduce peak loads, and meet energy-efficiency targets; however, privacy concerns have limited customer acceptance of these initiatives. The research objective of this project is to design appropriate architectures for information collection and dissemination with security and privacy guarantees and to develop state-of-the-art algorithms and protocols for privacy-preserving communication and control that effectively exploit the AMI for improved system operations and active customer participation.