Privacy-Preserving Epidemiological Modeling on Mobile Graphs
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866929729411481600 |
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| author | Günther, Daniel Holz, Marco Judkewitz, Benjamin Möllering, Helen Pinkas, Benny Schneider, Thomas Suresh, Ajith |
| author_facet | Günther, Daniel Holz, Marco Judkewitz, Benjamin Möllering, Helen Pinkas, Benny Schneider, Thomas Suresh, Ajith |
| contents | The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, 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. Unfortunately, the scarcity of relevant empirical data, specifically detailed social contact graphs, hampered their predictive accuracy. As this data is inherently privacy-critical, a method is urgently needed to perform powerful epidemiological simulations on real-world contact graphs without disclosing any sensitive~information.
In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework enabling standard models for infectious disease on a population's real contact graph while keeping all contact information locally on the participants' devices. As a building block of independent interest, we present PIR-SUM, a novel extension to private information retrieval for secure download of element sums from a database. Our protocols are supported by a proof-of-concept implementation, demonstrating a 2-week simulation over half a million participants completed in 7 minutes, with each participant communicating less than 50 KB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_00539 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Epidemiological Modeling on Mobile Graphs Günther, Daniel Holz, Marco Judkewitz, Benjamin Möllering, Helen Pinkas, Benny Schneider, Thomas Suresh, Ajith Cryptography and Security Computers and Society Social and Information Networks The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, 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. Unfortunately, the scarcity of relevant empirical data, specifically detailed social contact graphs, hampered their predictive accuracy. As this data is inherently privacy-critical, a method is urgently needed to perform powerful epidemiological simulations on real-world contact graphs without disclosing any sensitive~information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework enabling standard models for infectious disease on a population's real contact graph while keeping all contact information locally on the participants' devices. As a building block of independent interest, we present PIR-SUM, a novel extension to private information retrieval for secure download of element sums from a database. Our protocols are supported by a proof-of-concept implementation, demonstrating a 2-week simulation over half a million participants completed in 7 minutes, with each participant communicating less than 50 KB. |
| title | Privacy-Preserving Epidemiological Modeling on Mobile Graphs |
| topic | Cryptography and Security Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2206.00539 |