Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0)

By A. Aktay, S. Bavadekar, Gwen Cossoul, John Davis, Damien Desfontaines, Alex Fabrikant, Evgeniy Gabrilovich, Krishna Gadepalli, Bryant Gipson, Miguel Guevara and 14 more. arXiv.org, 2020

449 citations recorded by the source corpus.

This document describes the aggregation and anonymization process applied to the initial version of Google COVID-19 Community Mobility Reports (published at this http URL on April 2, 2020), a publicly available resource intended to help public health authorities understand what has changed in response to work-from-home, shelter-in-place, and other recommended policies aimed at flattening the curve of the COVID-19 pandemic. Our anonymization process is designed to ensure that no personal data, including an individual's location, movement, or contacts, can be derived from the resulting metrics. The high-level description of the procedure is as follows: we first generate a set of anonymized metrics from the data of Google users who opted in to Location History. Then, we compute percentage changes of these metrics from a baseline based on the historical part of the anonymized metrics. We then discard a subset which does not meet our bar for statistical reliability, and release the rest publicly in a format that compares the result to the private baseline.

Subfields: Epidemiology, Modeling and Simulation, Transportation

View Paper: https://www.semanticscholar.org/paper/858fc3babc8436bf2936968b37b7a8ea3049c6f1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale (2020, background): “Google - Mobility Reports [26] Global (0.”