Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs

Daniel Günther, Marco Holz, Benjamin Judkewitz, Helen Möllering, Benny Pinkas, Thomas Schneider, Ajith Suresh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationCCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
Number of pages3
ISBN (Electronic)9781450394505
StatePublished - 7 Nov 2022
Event28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 - Los Angeles, United States
Duration: 7 Nov 202211 Nov 2022

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security


Conference28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Country/TerritoryUnited States
CityLos Angeles


  • covid-19
  • decentralized epidemiological modeling
  • privacy
  • private information retrieval
  • trusted execution environments

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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