Günther, Daniel ; Holz, Marco ; Judkewitz, Benjamin ; Möllering, Helen ; Pinkas, Benny ; Schneider, Thomas ; Suresh, Ajith (2022)
POSTER: Privacy-preserving epidemiological modeling on mobile graph.
CCS '22: ACM SIGSAC Conference on Computer and Communications Security. Los Angeles, USA (07.-11.11.2022)
doi: 10.1145/3548606.3563497
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Günther, Daniel ; Holz, Marco ; Judkewitz, Benjamin ; Möllering, Helen ; Pinkas, Benny ; Schneider, Thomas ; Suresh, Ajith |
| Art des Eintrags: | Bibliographie |
| Titel: | POSTER: Privacy-preserving epidemiological modeling on mobile graph |
| Sprache: | Englisch |
| Publikationsjahr: | 7 November 2022 |
| Verlag: | ACM |
| Buchtitel: | CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security |
| Veranstaltungstitel: | CCS '22: ACM SIGSAC Conference on Computer and Communications Security |
| Veranstaltungsort: | Los Angeles, USA |
| Veranstaltungsdatum: | 07.-11.11.2022 |
| DOI: | 10.1145/3548606.3563497 |
| URL / URN: | https://dl.acm.org/doi/abs/10.1145/3548606.3563497 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Engineering, E4, E7, ATHENE, GRK Privacy&Trust for Mobile Users (Project A.1) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Praktische Kryptographie und Privatheit 20 Fachbereich Informatik > Kryptographische Protokolle DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche Profilbereiche Profilbereiche > Cybersicherheit (CYSEC) Forschungsfelder Forschungsfelder > Information and Intelligence Forschungsfelder > Information and Intelligence > Cybersecurity & Privacy DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1119: CROSSING – Kryptographiebasierte Sicherheitslösungen als Grundlage für Vertrauen in heutigen und zukünftigen IT-Systemen |
| Hinterlegungsdatum: | 21 Mär 2023 08:41 |
| Letzte Änderung: | 17 Jul 2023 08:42 |
| PPN: | 509743633 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum