The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, 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, the scarcity of relevant empirical data, specifically detailed social contact graphs, hampered their predictive accuracy. As this data is inherently privacy-critical, a method is urgently needed to perform powerful epidemiological simulations on real-world contact graphs without disclosing any sensitive information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework enabling standard models for infectious disease on a population’s real contact graph while keeping all contact information locally on the participants’ devices. As a building block of independent interest, we present PIR-SUM, a novel extension to private information retrieval for secure download of element sums from a database. Our protocols are supported by a proof-of-concept implementation, demonstrating a 2-week simulation over half a million participants completed in 7 minutes, with each participant communicating less than 50 KB.
Privacy-Preserving Epidemiological Modeling on Mobile Graphs
Daniel Gunther,Marco Holz,B. Judkewitz,Helen Mollering,Benny Pinkas,T. Schneider,Ajith Suresh
Published 2022 in IEEE Transactions on Information Forensics and Security
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- Publication year
2022
- Venue
IEEE Transactions on Information Forensics and Security
- Publication date
2022-06-01
- Fields of study
Medicine, Computer Science, Environmental Science
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