Protect Your Score: Contact Tracing With Differential Privacy Guarantees
Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling

TL;DR
This paper introduces a novel contact tracing algorithm that guarantees differential privacy when releasing risk scores, effectively balancing privacy concerns with public health benefits during a pandemic.
Contribution
It presents the first contact tracing method with differential privacy guarantees for COVID-19 risk scores, tested on major simulators with significant infection rate reductions.
Findings
Achieves 2-10x reduction in infection rates with epsilon=1 privacy
Demonstrates superior performance across various realistic scenarios
First to incorporate differential privacy in contact tracing risk scores
Abstract
The pandemic in 2020 and 2021 had enormous economic and societal consequences, and studies show that contact tracing algorithms can be key in the early containment of the virus. While large strides have been made towards more effective contact tracing algorithms, we argue that privacy concerns currently hold deployment back. The essence of a contact tracing algorithm constitutes the communication of a risk score. Yet, it is precisely the communication and release of this score to a user that an adversary can leverage to gauge the private health status of an individual. We pinpoint a realistic attack scenario and propose a contact tracing algorithm with differential privacy guarantees against this attack. The algorithm is tested on the two most widely used agent-based COVID19 simulators and demonstrates superior performance in a wide range of settings. Especially for realistic test…
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Taxonomy
TopicsCOVID-19 Digital Contact Tracing · Privacy-Preserving Technologies in Data · COVID-19 epidemiological studies
