# Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)

arXiv: https://arxiv.org/abs/2009.01265 · HTML: https://mlsanity.com/paper/2009.01265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Community search signatures as foundation features for human-centered geospatial modeling](https://mlsanity.com/paper/2410.22721/) (2024)
- [Combining digital data streams and epidemic networks for real time outbreak detection](https://mlsanity.com/paper/2511.07163/) (2025)
- [Correcting for heterogeneity in real-time epidemiological indicators](https://mlsanity.com/paper/2309.16546/) (2023)
- [Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning](https://mlsanity.com/paper/2606.22171/) (2026)

## Abstract (arXiv)

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.