“Exposure Notification (EN) Systems” which have been envisioned by a number of academic and industry groups, are useful in aiding health authorities worldwide to fight the COVID-19 pandemic spread via contact tracing. Among these systems, many rely on the BLE based Google-Apple Exposure Notification (GAEN) API (for iPhones and Android systems). We assert that it is now the time to investigate how to deal with scale issues, assuming the next pandemic/ variant will be more extensive. To this end, we present two modular enhancements to scale up the GAEN API by improving performance and suggesting a better performance-privacy tradeoff. Our modifications have the advantage of affecting only the GAEN API modules and do not require any change to the systems built on top of it, therefore it can be easily adopted upon emerging needs. The techniques we suggest in this paper (called “dice and splice” and “forest from the PRF-tree”) are general and applicable to scenarios of searching values within anonymous pseudo-randomly generated sequences.
|Title of host publication||Computer Security – ESORICS 2022 - 27th European Symposium on Research in Computer Security, Proceedings|
|Editors||Vijayalakshmi Atluri, Roberto Di Pietro, Christian D. Jensen, Weizhi Meng|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||19|
|State||Published - 2022|
|Event||27th European Symposium on Research in Computer Security, ESORICS 2022 - Virtual, Online|
Duration: 26 Sep 2022 → 30 Sep 2022
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||27th European Symposium on Research in Computer Security, ESORICS 2022|
|Period||26/09/22 → 30/09/22|
Bibliographical notePublisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.