Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)
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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.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2020.
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Citing papers per year: 2021: 3; 2022: 7; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
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- 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
- 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
- 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
- 2021An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprisescited by 6
- 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacycited by 6
- 2022Privacy-Aware Human Mobility Prediction via Adversarial Networkscited by 2
- 2021COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Predictioncited by 1
- 2021A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centrescited by 0
- 2022A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19cited by 0
- 2022Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learningcited by 0
- 2022M2M: A general method to perform various data analysis tasks from a differentially private sketchcited by 0
- 2024Unified Locational Differential Privacy Frameworkcited by 0
- 2026Differentially Private Modeling of Disease Transmission within Human Contact Networks8 Apr 2026
- 2024Unified Locational Differential Privacy Framework6 May 2024
- 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data30 Oct 2023
- 2022M2M: A general method to perform various data analysis tasks from a differentially private sketch25 Nov 2022
- 2022Data-Centric Epidemic Forecasting: A Survey19 Jul 2022
- 2022Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning2 Apr 2022
- 2022A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-198 Mar 2022
- 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey16 Feb 2022
- 2022Privacy-Aware Human Mobility Prediction via Adversarial Networks19 Jan 2022
- 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy4 Jan 2022
- 2021A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres20 Aug 2021
- 2021An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises15 Jun 2021
1 more
- 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
- 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
- 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
- 2021An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprisescited by 6
- 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacycited by 6
- 2022Privacy-Aware Human Mobility Prediction via Adversarial Networkscited by 2
- 2021COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Predictioncited by 1
- 2021A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centrescited by 0
- 2022A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19cited by 0
- 2022Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learningcited by 0
- 2022M2M: A general method to perform various data analysis tasks from a differentially private sketchcited by 0
- 2024Unified Locational Differential Privacy Frameworkcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Differentially Private Modeling of Disease Transmission within Human Contact Networks8 Apr 2026
- 2024Unified Locational Differential Privacy Framework6 May 2024
- 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data30 Oct 2023
- 2022M2M: A general method to perform various data analysis tasks from a differentially private sketch25 Nov 2022
- 2022Data-Centric Epidemic Forecasting: A Survey19 Jul 2022
- 2022Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning2 Apr 2022
- 2022A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-198 Mar 2022
- 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey16 Feb 2022
- 2022Privacy-Aware Human Mobility Prediction via Adversarial Networks19 Jan 2022
- 2022Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy4 Jan 2022
- 2021A Multi-Task Learning Framework for COVID-19 Monitoring and Prediction of PPE Demand in Community Health Centres20 Aug 2021
- 2021An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises15 Jun 2021
12 newest of 13 All 13 (JSON)