Decentralized Privacy-Preserving Proximity Tracing
Carmela Troncoso, Mathias Payer, Jean‐Pierre Hubaux, Marcel Salathé, James R. Larus, Edouard Bugnion, Wouter Lueks, Theresa Stadler, Apostolos Pyrgelis, Daniele Antonioli, Ludovic Barman, Sylvain Chatel, Kenneth G. Paterson, Srđjan Čapkun, David Basin, Jan Beutel, Dennis Jackson, Marc Roeschlin, P. Leu, Bart Preneel, Nigel P. Smart, Aysajan Abidin, Seda Gürses, Michael Veale, Cas Cremers, Michael Backes, Nils Ole Tippenhauer, Reuben Binns, Ciro Cattuto, Alain Barrat, Dario Fiore, Manuel Barbosa, Rui Oliveira, José Pereira
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This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims t ...
o minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of our proximity tracing system is to determine who has been in close physical proximity to a COVID-19 positive person and thus exposed to the virus, without revealing the contact's identity or where the contact occurred. To achieve this goal, users run a smartphone app that continually broadcasts an ephemeral, pseudo-random ID representing the user's phone and also records the pseudo-random IDs observed from smartphones in close proximity. When a patient is diagnosed with COVID-19, she can upload pseudo-random IDs previously broadcast from her phone to a central server. Prior to the upload, all data remains exclusively on the user's phone. Other users' apps can use data from the server to locally estimate whether the device's owner was exposed to the virus through close-range physical proximity to a COVID-19 positive person who has uploaded their data. In case the app detects a high risk, it will inform the user.
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This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale.
Implementation Evidence Summary
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
Reproduction Risks
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Hardware Notes
Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Evidence disclosure
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation Status
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- Start from related paper: Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing.
- Track assumptions and missing details in an experiment log before coding.
Reproduction readiness
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
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Research context
157
Citations
0
References
Tasks
Upload, Phone, Computer science, Contact tracing, Tracing, Internet privacy, Encryption, Coronavirus disease 2019 (COVID-19)
Methods
None detected
Domains
Computer security
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