Decentralized Privacy-Preserving Proximity Tracing
Creators: name: Troncoso, Carmela
name: Payer, Mathias
name: Hubaux, Jean-Pierre
name: Salathé, Marcel
name: Larus, James
name: Stadler, Theresa
name: Pyrgelis, Apostolos
name: Antonioli, Daniele
name: Barman, Ludovic
name: Chatel, Sylvain
name: Paterson, Kenneth
name: Capkun, Srdjan
name: Basin, David
name: Beutel, Jan
name: Jackson, Dennis
name: Roeschlin, Marc
name: Leu, Patrick
name: Preneel, Bart
name: Smart, Nigel
name: Abidin, Aysajan
name: Gürses, Seda
name: Veale, Michael
name: Cremers, Cas
name: Backes, Michael
name: Tippenhauer, Nils Ole
name: Binns, Reuben
name: Cattuto, Ciro
name: Barrat, Alain
name: Fiore, Dario
name: Barbosa, Manuel
name: Oliveira, Rui
name: Pereira, José
Publisher: name: CISPA
Year: 2023

description: This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale.This system 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 to 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. The system provides the following security and privacy protections: •Ensures data minimization. The central server only observes anonymous identifiers of COVID-19 positive users without any proximity information. Health authorities learn no information except that provided when a user reaches out to them after being notified. •Prevents abuse of data. As the central server receives the minimum amount of information tailored to its requirements, it can neither misuse the collected data for other purposes, nor can it be coerced or subpoenaed to make other data available. •Prevents tracking of users. No entity can track users that have not reported a positive diagnosis.Depending on the implementation chosen, others can only track COVID-19 positive users in a small geographical region limited by their capability to deploy infrastructure that can receive broadcasted Bluetooth beacons. •Graceful dismantling. The system will dismantle itself after the end of the epidemic. COVID-19 positive users will stop uploading their data to the central server, and people will stop using the app. Data on the server and in the apps is removed after 14 days. We are publishing this document to inform the discussion revolving around the design and implementation of proximity tracing systems. This document is accompanied by other documents containing an overview of the data protection compliance of the design, an extensive privacy and security risk evaluation of digital proximity tracing systems, a proposal for interoperability of multiple systems deployed in different geographical regions,and alternatives for developing secure upload authorisation mechanisms.