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Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Published: 07 November 2022 Publication History

Abstract

Over the last two years, governments all over the world have used a variety of containment measures to control the spread of \covid, 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 in actuality. Unfortunately, their predictive accuracy is hampered by the scarcity of relevant empirical data, concretely 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 a wide range of standard epidemiological models for any infectious disease on a population's most recent real contact graph while keeping all contact information private locally on the participants' devices. Our theoretical constructs are supported by a proof-of-concept implementation in which we show that a 2-week simulation over a population of half a million can be finished in 7 minutes with each participant consuming less than 50 KB of data.

References

[1]
David Adam. 2020. Special report: The simulations driving the world's response to COVID-19. Nature (2020).
[2]
Nadeem Ahmed, Regio A Michelin, Wanli Xue, Sushmita Ruj, Robert Malaney, Salil S Kanhere, Aruna Seneviratne, Wen Hu, Helge Janicke, and Sanjay K Jha. 2020. A Survey of COVID-19 Contact Tracing Apps. IEEE Access (2020).
[3]
Giulia Giordano, Franco Blanchini, Raffaele Bruno, Patrizio Colaneri, Alessandro Di Filippo, Angela Di Matteo, and Marta Colaneri. 2020. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nature Medicine (2020).
[4]
Daniel Gü nther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider, and Ajith Suresh. 2022. Privacy-Preserving Epidemiological Modeling on Mobile Graphs. (2022). https://doi.org/10.48550/arXiv.2206.00539
[5]
Petra Klepac, Adam J Kucharski, Andrew JK Conlan, Stephen Kissler, Maria L Tang, Hannah Fry, and Julia R Gog. 2020. Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project. MedRxiv (2020).
[6]
Dyani Lewis. 2020. Where Covid contract-tracing went wrong. Nature (2020).
[7]
Dominika Maison, Diana Jaworska, Dominika Adamczyk, and Daria Affeltowicz. 2021. The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study. PloS One (2021).
[8]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In International Conference on Artificial Intelligence and Statistics.
[9]
Robin N. Thompson. 2020. Epidemiological models are important tools for guiding COVID-19 interventions. BMC Medicine, Vol. 18, 1 (2020), 152.
[10]
Paul Tupper, Sarah P. Otto, and Caroline Colijn. 2021. Fundamental Limitations of Contact Tracing for COVID-19. FACETS (2021).
[11]
Andjela Blagojević, Danijela Cvetković, Aleksandar Cvetković, Ivan Lorencin, Sandi Baressi ?egota, Dragan Milovanović, Dejan Baskić, Zlatan Car, and Nenad Filipović. 2021. Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union. Frontiers in Public Health, Vol. 9 (2021).

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  • (2023)FLUTE: Fast and Secure Lookup Table Evaluations2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179345(515-533)Online publication date: May-2023

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  1. Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs

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      cover image ACM Conferences
      CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
      November 2022
      3598 pages
      ISBN:9781450394505
      DOI:10.1145/3548606
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 07 November 2022

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      Author Tags

      1. covid-19
      2. decentralized epidemiological modeling
      3. privacy
      4. private information retrieval
      5. trusted execution environments

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      • Deutsche Forschungsgemeinschaft (DFG)
      • European Union's Horizon 2020 research and innovation program

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      • (2023)FLUTE: Fast and Secure Lookup Table Evaluations2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179345(515-533)Online publication date: May-2023

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