Scaling up GAEN Pseudorandom Processes: Preparing for a More Extensive Pandemic
Abstract
“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.
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.
Research Areas
