Releasing Large-Scale Human Mobility Histograms with Differential Privacy
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913416363376640 |
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| author | Bian, Christopher Cheu, Albert Guzman, Yannis Gruteser, Marco Kairouz, Peter McKenna, Ryan Roth, Edo |
| author_facet | Bian, Christopher Cheu, Albert Guzman, Yannis Gruteser, Marco Kairouz, Peter McKenna, Ryan Roth, Edo |
| contents | Environmental Insights Explorer (EIE) is a Google product that reports aggregate statistics about human mobility, including various methods of transit used by people across roughly 50,000 regions globally. These statistics are used to estimate carbon emissions and provided to policymakers to inform their decisions on transportation policy and infrastructure. Due to the inherent sensitivity of this type of user data, it is crucial that the statistics derived and released from it are computed with appropriate privacy protections. In this work, we use a combination of federated analytics and differential privacy to release these required statistics, while operating under strict error constraints to ensure utility for downstream stakeholders. In this work, we propose a new mechanism that achieves $ ε\approx 2 $-DP while satisfying these strict utility constraints, greatly improving over natural baselines. We believe this mechanism may be of more general interest for the broad class of group-by-sum workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03496 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Releasing Large-Scale Human Mobility Histograms with Differential Privacy Bian, Christopher Cheu, Albert Guzman, Yannis Gruteser, Marco Kairouz, Peter McKenna, Ryan Roth, Edo Cryptography and Security Databases Environmental Insights Explorer (EIE) is a Google product that reports aggregate statistics about human mobility, including various methods of transit used by people across roughly 50,000 regions globally. These statistics are used to estimate carbon emissions and provided to policymakers to inform their decisions on transportation policy and infrastructure. Due to the inherent sensitivity of this type of user data, it is crucial that the statistics derived and released from it are computed with appropriate privacy protections. In this work, we use a combination of federated analytics and differential privacy to release these required statistics, while operating under strict error constraints to ensure utility for downstream stakeholders. In this work, we propose a new mechanism that achieves $ ε\approx 2 $-DP while satisfying these strict utility constraints, greatly improving over natural baselines. We believe this mechanism may be of more general interest for the broad class of group-by-sum workloads. |
| title | Releasing Large-Scale Human Mobility Histograms with Differential Privacy |
| topic | Cryptography and Security Databases |
| url | https://arxiv.org/abs/2407.03496 |