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Paper 2020/1546

PEM: Privacy-preserving Epidemiological Modeling

Marco Holz and Benjamin Judkewitz and Helen Möllering and Benny Pinkas and Thomas Schneider

Abstract

Modeling the spread of COVID-19 is crucial for any effort to manage the pandemic. However, detailed epidemiological simulations suffer from a scarcity of relevant empirical data, such as social contact graphs, because such data is inherently privacy-critical. Thus, there is an urgent need for a method to perform powerful epidemiological simulations on real-world contact graphs without disclosing privacy-critical information. In this work, we propose a practical framework for privacy-preserving epidemiological modeling (PEM) on contact information stored on mobile phones, like the ones collected by already deployed contact tracing apps. Unlike those apps, PEM allows for meaningful epidemiological simulations. This is enabled by a novel Threshold-PIR-SUM protocol to privately retrieve the sum of a fixed number of distinct values without revealing individual values. PEM protects the privacy of the users by not revealing sensitive data to the system operator or other participants, while enabling detailed predictive models of pandemic spread.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Preprint. MINOR revision.
Keywords
Decentralized Epidemiological ModelingPrivacyPrivate Information RetrievalCOVID-19
Contact author(s)
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
History
2023-07-26: last of 6 revisions
2020-12-13: received
See all versions
Short URL
https://ia.cr/2020/1546
License
Creative Commons Attribution
CC BY
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