Reichert, Leonie ; Pazelt, Marcel ; Scheuermann, Björn (2021)
Circuit-based PSI for Covid-19 Risk Scoring.
2021 IEEE International Performance, Computing, and Communications Conference. virtual Conference (29.10.2021 - 31.10.2021)
doi: 10.1109/IPCCC51483.2021.9679360
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Many solutions have been proposed to improve manual contact tracing for infectious diseases through automation. Privacy is crucial for the deployment of such a system as it greatly influences adoption. Approaches for digital contact tracing like Google Apple Exposure Notification (GAEN) protect the privacy of users by decentralizing risk scoring. But GAEN leaks information about diagnosed users as ephemeral pseudonyms are broadcast to everyone. To combat deanonymisation based on the time of encounter while providing extensive risk scoring functionality we propose to use a private set intersection (PSI) protocol based on garbled circuits. Using oblivious programmable pseudo random functions PSI (OPPRF-PSI) , we implement our solution CERTAIN which leaks no information to querying users other than one risk score for each of the last 14 days representing their risk of infection. We implement payload inclusion for OPPRF-PSI and evaluate the efficiency and performance of different risk scoring mechanisms on an Android device.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Reichert, Leonie ; Pazelt, Marcel ; Scheuermann, Björn |
| Art des Eintrags: | Bibliographie |
| Titel: | Circuit-based PSI for Covid-19 Risk Scoring |
| Sprache: | Englisch |
| Publikationsjahr: | 29 Oktober 2021 |
| Verlag: | IEEE |
| Buchtitel: | 021 IEEE International Performance, Computing, and Communications Conference (IPCCC 2021) |
| Veranstaltungstitel: | 2021 IEEE International Performance, Computing, and Communications Conference |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 29.10.2021 - 31.10.2021 |
| DOI: | 10.1109/IPCCC51483.2021.9679360 |
| Kurzbeschreibung (Abstract): | Many solutions have been proposed to improve manual contact tracing for infectious diseases through automation. Privacy is crucial for the deployment of such a system as it greatly influences adoption. Approaches for digital contact tracing like Google Apple Exposure Notification (GAEN) protect the privacy of users by decentralizing risk scoring. But GAEN leaks information about diagnosed users as ephemeral pseudonyms are broadcast to everyone. To combat deanonymisation based on the time of encounter while providing extensive risk scoring functionality we propose to use a private set intersection (PSI) protocol based on garbled circuits. Using oblivious programmable pseudo random functions PSI (OPPRF-PSI) , we implement our solution CERTAIN which leaks no information to querying users other than one risk score for each of the last 14 days representing their risk of infection. We implement payload inclusion for OPPRF-PSI and evaluate the efficiency and performance of different risk scoring mechanisms on an Android device. |
| Freie Schlagworte: | reichert, Reichert |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Kommunikationsnetze |
| Hinterlegungsdatum: | 23 Mai 2024 13:30 |
| Letzte Änderung: | 08 Okt 2024 12:38 |
| PPN: | 522025528 |
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