Abstract The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a Call Details Record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Voronoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3–5 of its recorded locations. We investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: The device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. Combining data on the distance distribution of the number of connections available for each antenna in the network, we resolve the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone privacy and new full set of tools for data analysis.

中文翻译：

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手机网络中的概率定位及其对移动数据隐私的影响

摘要 移动电话定位的传统方法是基于这样的假设，即呼叫详细记录 (CDR) 中记录的蜂窝塔的地理位置是设备位置的代理。然后基于整个蜂窝塔网络构建 Voronoi 细分，该细分被视为坐标系，设备位于记录在 CDR 中的蜂窝塔的 Voronoi 多边形中。如果基于 Voronoi 的定位正确，则设备轨迹的唯一性非常高，可以根据设备记录的 3-5 个位置来识别设备。我们研究了一种设备定位的概率方法，该方法基于对每个天线参数和连接数量的了解，取决于到天线的距离。基于 Voronoi 的布局与现实世界布局之间的关键区别在于天线服务区域的基本重叠：位于蜂窝塔多边形中的设备可以由网络系统选择的更远的天线提供服务以平衡网络负载。结合网络中每个天线可用连接数的距离分布数据，我们通过应用贝叶斯推理解决重叠问题并构建设备位置的真实分布。概率设备定位需要全面修订手机隐私和新的全套数据分析工具。s 多边形可以由更远的天线提供服务，该天线由网络系统选择以平衡网络负载。结合网络中每个天线可用连接数的距离分布数据，我们通过应用贝叶斯推理解决重叠问题并构建设备位置的真实分布。概率设备定位需要全面修订手机隐私和新的全套数据分析工具。s 多边形可以由更远的天线提供服务，该天线由网络系统选择以平衡网络负载。结合网络中每个天线可用连接数的距离分布数据，我们通过应用贝叶斯推理解决重叠问题并构建设备位置的真实分布。概率设备定位需要全面修订手机隐私和新的全套数据分析工具。