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Checking Contact Tracing App Implementations with Bespoke Static Analysis.
Flood, Robert; Chan, Sheung Chi; Chen, Wei; Aspinall, David.
Affiliation
  • Flood R; LFCS, University of Edinburgh, Edinburgh, Scotland, UK.
  • Chan SC; MACS, Heriot-Watt University, Edinburgh, Scotland, UK.
  • Chen W; LFCS, University of Edinburgh, Edinburgh, Scotland, UK.
  • Aspinall D; LFCS, University of Edinburgh, Edinburgh, Scotland, UK.
SN Comput Sci ; 3(6): 496, 2022.
Article in En | MEDLINE | ID: mdl-36193263
ABSTRACT
In the wake of the COVID-19 pandemic, contact tracing apps have been developed based on digital contact tracing frameworks. These allow developers to build privacy-conscious apps that detect whether an infected individual is in close proximity with others. Given the urgency of the problem, these apps have been developed at an accelerated rate with a brief testing period. Such quick development may have led to mistakes in the apps' implementations, resulting in problems with their functionality, privacy and security. To mitigate these concerns, we develop and apply a methodology for evaluating the functionality, privacy and security of Android apps using the Google/Apple Exposure Notification API. This is a three-pronged approach consisting of a manual analysis, general static analysis and a bespoke static analysis, using a tool we have developed, dubbed MonSTER. As a result, we have found that, although most apps met the basic standards outlined by Google/Apple, there are issues with the functionality of some of these apps that could impact user safety.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: SN Comput Sci Year: 2022 Document type: Article Affiliation country: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: SN Comput Sci Year: 2022 Document type: Article Affiliation country: United kingdom