scholarly journals Feature selection for proximity estimation in COVID-19 contact tracing apps based on Bluetooth Low Energy (BLE)

2021 ◽  
pp. 101474
Author(s):  
Pablo G. Madoery ◽  
Ramiro Detke ◽  
Lucas Blanco ◽  
Sandro Comerci ◽  
Juan Fraire ◽  
...  
2021 ◽  
Vol 2 (2) ◽  
pp. 1-33 ◽  
Author(s):  
Leonie Reichert ◽  
Samuel Brack ◽  
BjÖRN Scheuermann

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing . A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.


2022 ◽  
Vol 8 (2) ◽  
pp. 1-27
Author(s):  
Qiang Tang

In the current COVID-19 pandemic, manual contact tracing has been proven to be very helpful to reach close contacts of infected users and slow down spread of the virus. To improve its scalability, a number of automated contact tracing (ACT) solutions have been proposed, and some of them have been deployed. Despite the dedicated efforts, security and privacy issues of these solutions are still open and under intensive debate. In this article, we examine the ACT concept from a broader perspective, by focusing on not only security and privacy issues but also functional issues such as interface, usability, and coverage. We first elaborate on these issues and particularly point out the inevitable privacy leakages in existing Bluetooth Low Energy based ACT solutions, including centralized and decentralized ones. In addition, we examine the existing venue-based ACT solutions and identify their privacy and security concerns. Then, we propose a generic venue-based ACT solution and a concrete instantiation based on Bluetooth Low Energy technology. Our solution monitors users’ contacting history only in virus-spreading-prone venues and offers higher-level protection for both security and privacy than its predecessors. Finally, we evaluate our solution from security, privacy, and efficiency perspectives, and also highlight how to reduce false positives in some specific indoor environments.


2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Felix Sattler ◽  
Jackie Ma ◽  
Patrick Wagner ◽  
David Neumann ◽  
Markus Wenzel ◽  
...  

Abstract Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.


2021 ◽  
Vol 4 ◽  
Author(s):  
Guanglin Tang ◽  
Kenneth Westover ◽  
Steve Jiang

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.


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