基于差分隐私发布大规模人类出行直方图
Releasing Large-Scale Human Mobility Histograms with Differential Privacy
AI总结：
本文针对谷歌环境洞察探索者发布人类出行聚合统计数据的隐私需求，提出一种结合联邦分析与差分隐私的新机制，在约ε≈2的DP保证下满足严格误差约束，显著优于基线方法，并适用于group-by-sum类工作负载。
摘要：
环境洞察探索者（Environmental Insights Explorer，EIE）是谷歌的一款产品，用于报告人类出行的聚合统计数据，包括全球约50,000个地区中人们使用的各种交通方式。这些统计数据被用于估算碳排放，并提供给政策制定者，以帮助其制定交通政策和基础设施相关决策。由于此类用户数据具有固有的敏感性，因此从数据中推导并发布统计数据时，必须采用适当的隐私保护措施。在本工作中，我们结合使用联邦分析和差分隐私来发布这些所需统计数据，同时在严格的误差约束下运行，以确保对下游利益相关者的实用性。在本工作中，我们提出了一种新机制，在满足这些严格实用性约束的同时实现约 ε≈2 的差分隐私（DP），相比自然基线方法有显著改进。我们认为该机制对于更广泛的 group-by-sum 工作负载类别可能具有更普遍的意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
