Privacy-Preserving Epidemiological Modeling on Mobile Graphs
- URL: http://arxiv.org/abs/2206.00539v1
- Date: Wed, 1 Jun 2022 14:51:17 GMT
- Title: Privacy-Preserving Epidemiological Modeling on Mobile Graphs
- Authors: Daniel G\"unther, Marco Holz, Benjamin Judkewitz, Helen M\"ollering,
Benny Pinkas, Thomas Schneider, Ajith Suresh
- Abstract summary: We present RIPPLE, a privacy-preserving epidemiological modeling framework.
We also present PIR-SUM, a novel extension to private information retrieval.
- Score: 17.085245461105863
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Over the last two years, governments all over the world have used a variety
of containment measures to control the spread of COVID-19, 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.
In this regard, we also present PIR-SUM, a novel extension to private
information retrieval that allows users to securely download the sum of a set
of elements from a database rather than individual elements. 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.
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