动态空间采样与地理围栏监控问题
The Problem of Dynamic Spatial Sampling and Geofence Surveillance
AI总结：
本文提出最优半径估计器，用于动态空间采样和地理围栏监控，以平衡执法需求与个人隐私。
摘要：
地理围栏监控提出了动态空间采样问题。执法部门必须建立地理围栏边界以识别相关嫌疑人。这需要确定一个采样区域围绕监控点，并通过地理位置标签计数交集个体数量。执法部门通常使用固定距离间隔或固定多边形边界构建采样区域。这引发了隐私问题，因为考虑构建这些边界时未考虑当地的人口密度，如行人流量或交通模式。这增加了选择性扩展的风险，即机构试图扩展数据收集范围超过先前批准的授权。本文试图通过提出一组最优半径估计器来平衡执法的监控需求与个人层面的隐私。这些插件估计器使用人类活动模式的经验分布来估计最优半径。给定监控点和点密度集，最优半径生成适应本地条件的监控边界。我们讨论了将此估计器应用于警务监控努力的影响，以及执法部门如何使用算法更好地保护公民隐私。
英文摘要：
Geofencing surveillance poses a dynamic spatial sampling problem. Police agencies must select a surveillance site, choose a geofence perimeter from a set of alternatives, and identify potential suspects through reverse location warrants. At the same time, warrant magistrates must impose constraints that curtail the reach of police surveillance efforts. This sampling problem emerges because agencies commonly use fixed geofence boundaries that ignore how humans move about a chosen surveillance site (i.e., pedestrian flows or traffic patterns). This further exacerbates privacy concerns and increases the risk of selective expansion where agencies extend their data collection efforts beyond the parameters outlined in their warrant. Given the Court's recent ruling in Chatrie, there is currently a need to establish a measurable process that allows magistrates to quantify and evaluate the potential impacts of a warrant proposal. In this paper, we take the first step in introducing a set of optimal radius estimators that measure how geofence perimeters adapt to their local context. Given a surveillance site and some privacy constraint, these estimators generate surveillance perimeters whose size changes with local population densities. This allows magistrates to quantify tradeoffs between local privacy intrusions with law enforcement's surveillance needs. We discuss the properties of these estimators, their underlying assumptions, and the potential consequences of using algorithms to better protect the privacy of its citizens.
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