Cryptology ePrint Archive: Report 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.

Category / Keywords: applications / Decentralized Epidemiological Modeling, Privacy, Private Information Retrieval, COVID-19

Date: received 11 Dec 2020

Contact author: holz at encrypto cs tu-darmstadt de, benjamin judkewitz@charite de, moellering@encrypto cs tu-darmstadt de, benny@pinkas net, schneider@encrypto cs tu-darmstadt de

Available format(s): PDF | BibTeX Citation

Version: 20201213:165907 (All versions of this report)

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