Explainable Link Prediction for Privacy-Preserving Contact Tracing

• URL: http://arxiv.org/abs/2012.05516v1
• Date: Thu, 10 Dec 2020 08:58:24 GMT
• Title: Explainable Link Prediction for Privacy-Preserving Contact Tracing
• Authors: Balaji Ganesan, Hima Patel, Sameep Mehta
• Abstract summary: Contact tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. We present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.
• Score: 5.866574931696403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.

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