Poster: Privacy-Preserving Epidemiological Modeling on Mobile Graphs
28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
; : 3351-3353, 2022.
Article
in English
| Scopus | ID: covidwho-2162012
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. © 2022 Owner/Author.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Year:
2022
Document Type:
Article
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