Pintér Gergő
ANETI Lab, Corvinus Institute for Advanced Studies, Corvinus University of Budapest, Budapest, 1093, Hungary.
Sci Rep. 2024 Dec 28;14(1):30948. doi: 10.1038/s41598-024-82006-5.
Researchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals.
研究人员面临着在发表论文时公布移动性数据与保护个人隐私之间的权衡。除了匿名化过程外，还应用了其他技术，如空间离散化和位置隐藏或删除，以实现这两个双重目标。主要研究问题是，隐藏观察区域是否是一种充分的保护形式，或者城市地区的人类移动模式是否本质上会暴露位置。移动性数据的特征，如给定空间单元中的活动记录数量，可以揭示城市景观的轮廓，这可用于推断相关城市的身份。所提出的定位方法在多个城市使用不同的开放数据集和更粗的空间离散化单元进行了测试。虽然公布移动性数据对研究至关重要，但隐藏观察区域不足以防止城市区域被识别。不应模糊观察区域，而应向轨迹添加噪声，以降低与个人相关的隐私风险。
