Decentralized Privacy-Preserving Proximity Tracing Version: 12th April 2020.
Contact the first author for the latest version. EPFL: Prof. Carmela Troncoso, Prof. Mathias Payer, Prof. Jean-Pierre Hubaux, Prof. Marcel Salathé, Prof. James Larus, Prof. Edouard Bugnion, Dr. Wouter Lueks, Theresa Stadler, Dr. Apostolos Pyrgelis, Dr. Daniele Antonioli, Ludovic Barman, Sylvain Chatel ETHZ: Prof. Kenneth Paterson, Prof. Srdjan Čapkun, Prof. David Basin, Dr. Jan Beutel, Dennis Jackson KU Leuven: Prof. Bart Preneel, Prof. Nigel Smart, Dr. Dave Singelee, Dr. Aysajan Abidin TU Delft: Prof. Seda Guerses
University College London: Dr. Michael Veale CISPA: Prof. Cas Cremers
University of Oxford: Dr. Reuben Binns University of Torino / ISI Foundation Prof. Ciro Cattuto CC-BY 4.0 Executive Summary This document proposes a system for secure and privacy-preserving proximity tracing (aka contact tracing) at large scale. This system provides a technological foundation to help slow the spread of the SARS-CoV-2 virus by simplifying and accelerating the process of notifying people who have been in contact with an infected person. The system design aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of proximity tracing is to determine who has been in close physical proximity to an infected person, without revealing the contact’s identity or where this contact occurred. To achieve this goal, users continually run a smartphone app that broadcasts an ephemeral, pseudo-random ID representing the user and also record pseudo-random IDs observed from smartphones in close proximity. Whenever a patient is diagnosed for COVID-19, she can upload some anonymous data from her phone to a central server. This step should only be done with the approval of a health authority and the explicit permission of the individual. Prior to the upload, all data remains exclusively on the user’s phone. Other instances of the app can use the anonymous data from the server to locally compute whether the app’s user was in physical proximity to an infected person and the risk that an encounter led to a propagation of the virus. In case the app detects a high risk, it will inform the user. Additionally, the system enables users to voluntarily provide information to epidemiologists, in a privacy-preserving manner, to enable studies of the evolution of the disease and to assist in finding better policies to prevent further infections.