# Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)

arXiv: https://arxiv.org/abs/2004.04145 · HTML: https://mlsanity.com/paper/2004.04145/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data](https://mlsanity.com/paper/2310.19250/) (2023)
- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)
- [An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises](https://mlsanity.com/paper/2106.10241/) (2021)
- [Privacy-Aware Human Mobility Prediction via Adversarial Networks](https://mlsanity.com/paper/2201.07519/) (2022)
- [COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction](https://mlsanity.com/paper/2105.00620/) (2021)
- [A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres](https://mlsanity.com/paper/2108.09402/) (2021)
- [A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19](https://mlsanity.com/paper/2203.04383/) (2022)
- [Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning](https://mlsanity.com/paper/2204.00850/) (2022)
- [M2M: A general method to perform various data analysis tasks from a differentially private sketch](https://mlsanity.com/paper/2211.14062/) (2022)
- [Unified Locational Differential Privacy Framework](https://mlsanity.com/paper/2405.03903/) (2024)
- [Differentially Private Modeling of Disease Transmission within Human Contact Networks](https://mlsanity.com/paper/2604.07493/) (2026)

## Abstract (arXiv)

This document describes the aggregation and anonymization process applied to the initial version of Google COVID-19 Community Mobility Reports (published at http://google.com/covid19/mobility 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.