Paper 2020/1546

Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Abstract

Since 2020, governments all over the world have used a variety of containment measures to control the spread of COVID-19, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, specifically detailed social contact graphs. As this data is inherently privacy-critical, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing sensitive~information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework that enables the execution of standard epidemiological models for infectious disease on a population's most recent real contact graph while keeping all contact information privately and locally on the participants' devices. As underlying building block, we present PIR-SUM, a novel extension to private information retrieval that allows users to securely download the sum of a set of elements from a database rather than individual elements. We provide a proof-of-concept implementation of our protocols demonstrating that a 2-week simulation over a population of half a million can be finished in 7 minutes, with each participant communicating less than 50 KB of data.