Paper 2020/1546

Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Daniel Günther, TU Darmstadt
Marco Holz, TU Darmstadt
Benjamin Judkewitz, Charité-Universitätsmedizin
Helen Möllering, TU Darmstadt
Benny Pinkas, Bar-Ilan University
Thomas Schneider, TU Darmstadt
Ajith Suresh, TU Darmstadt

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.

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Decentralized Epidemiological ModelingPrivacyPrivate Information Retrieval
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guenther @ encrypto cs tu-darmstadt de
holz @ encrypto cs tu-darmstadt de
benjamin judkewitz @ charite de
moellering @ encrypto cs tu-darmstadt de
benny @ pinkas net
schneider @ encrypto cs tu-darmstadt de
suresh @ encrypto cs tu-darmstadt de
2024-06-10: last of 7 revisions
2020-12-13: received
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      author = {Daniel Günther and Marco Holz and Benjamin Judkewitz and Helen Möllering and Benny Pinkas and Thomas Schneider and Ajith Suresh},
      title = {Privacy-Preserving Epidemiological Modeling on Mobile Graphs},
      howpublished = {Cryptology ePrint Archive, Paper 2020/1546},
      year = {2020},
      note = {\url{}},
      url = {}
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