Explainable Link Prediction for Privacy-Preserving Contact Tracing

2020/12/10 by Balaji Ganesan, Ganesan, Balaji, Hima Patel +3
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #COVID-19 Digital Contact Tracing #Cryptography and Security (cs.CR) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2012.05516

openalex publication_date 2020/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Inductive Representation Learning on Large Graphs
• Fast Graph Representation Learning with PyTorch Geometric
• On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Privacy-Preserving Contact Tracing: current solutions and open questions

Related

• DNA: Differentially private Neural Augmentation for contact tracing
• Trust and Transparency in Contact Tracing Applications
• TraceSecure: Towards Privacy Preserving Contact Tracing
• Predicting Infectiousness for Proactive Contact Tracing
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