Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)
Year: 2020
Type: preprint
Abstract: This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19symptomdataset on September 2, 2020), a publicly available dataset that shows aggregated, anonymized trends in Google searches for symptoms (and some related topics). The anonymization process is designed to protect the daily symptom search activity of every user with $\varepsilon$-differential privacy for $\varepsilon$ = 1.68.
Source: arXiv (Cornell University)
Authors: Bavadekar, Shailesh, Dai, Andrew, Davis, John, Desfontaines, Damien, Eckstein, Ilya, Everett, Katie, Fabrikant, Alex, Flores, Gerardo, Gabrilovich, Evgeniy, Gadepalli, Krishna, Glass, Shane, Huang, Rayman, Kamath, Chaitanya, Kraft, Dennis, Kumok, Akim, Marfatia, Hinali, Mayer, Yael, Miller, Benjamin, Pearce, Adam, Perera, Irippuge Milinda, Ramachandran, Venky, Raman, Karthik, Roessler, Thomas, Shafran, Izhak, Shekel, Tomer, Stanton, Charlotte, Stimes, Jacob, Sun, Mimi, Wellenius, Gregory, Zoghi, Masrour
Institution: Google (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3081925308