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1. Exploiting a Contact Tracing App to Attack Neighboring Devices

   Fitzgerald, J. (January 2023, Proceedings of the 56th Hawaii International Conference on System Sciences)

   The recent pandemic fosters an increasing dependency on various forms of digital communications that support social distancing. To mitigate widespread exposure to COVID, the Louisiana Department of Health’s COVID Defense contact tracing application helps users learn about potential exposures to infected individuals. This research investigates the viability of using the Louisiana Department of Health’s COVID Defense application symptoms share feature as an attack vector. The primary contribution of this research is an initial assessment of the effective modification and distribution of a packaged JSON file that contains a malicious link. Secondly, it highlights the effectiveness of this attack through email, WIFI direct, and nearby share. 

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2. Cellular-Assisted COVID-19 Contact Tracing

   https://doi.org/10.1145/3469258.3469848  

   Yi, Fan; Xie, Yaxiong; Jamieson, Kyle (June 2021, Proceedings of the 2nd Workshop on Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing (HealthDL))

   null (Ed.)

   The coronavirus disease (COVID-19) pandemic has caused social and economic upheaval around the world. Contact tracing is a proven effective way that health authorities may contain the spread of COVID-19, but is challenging for airborne disease. In this paper, we propose LTESafe, a cellular-assisted privacy-preserving COVID-19 contact tracing system. LTESafe leverages a deep neural network based feature extractor to map the cellular CSI to a high-dimensional feature space, within which the Euclidean distance between points indicates the proximity of devices. By doing so, we preserve user privacy by hiding the physical locations of smartphones and at the same time achieve high accuracy. Our preliminary experimental results demonstrate that LTESafe achieves an overall accuracy of 92.79% in determining whether two devices are within six feet proximity or not, and only misses 1.35% of close contacts. 

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3. Contact Tracing: A Low Cost Reconstruction Framework for Surface Contact Interpolation

   Lakshmipathy, A.; Bauer, D.; Pollard, N. (October 2021, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   We present a novel, low cost framework for reconstructing surface contact movements during in-hand manipulations. Unlike many existing methods focused on hand pose tracking, ours models the behavior of contact patches, and by doing so is the first to obtain detailed contact tracking estimates for multi-contact manipulations. Our framework is highly accessible, requiring only low cost, readily available paint materials, a single RGBD camera, and a simple, deterministic interpolation algorithm. Despite its simplicity, we demonstrate the framework’s effectiveness over the course of several manipulations on three common household items. Finally, we demonstrate the use of a generated contact time series in manipulation learning for a simulated robot hand. 

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4. Multiagent Epidemiologic Inference through Realtime Contact Tracing

   Sharon, G; Ault, J; Stone, P; Kompella, V; Capobianco, R (April 2021, Autonomous Agents and Multiagent Systems)

   null (Ed.)
5. COVIDFREE App: The User-Enabling Contact Prevention Application: A Review

   https://doi.org/10.25046/aj070215  

   Mimo, Edgard Musafiri; McDaniel, Troy; Ruvunangiza, Jeremie Biringanine (April 2022, Advances in Science, Technology and Engineering Systems Journal)