BioComplex Laboratory, Department of Computer Science, University of Exeter, Exeter, UK.
University of Edinburgh Business School, Edinburgh, UK.
Nat Commun. 2022 Apr 8;13(1):1922. doi: 10.1038/s41467-022-29592-y.
Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual's movement can be present in the mobility patterns of both acquaintances and strangers. We develop a "colocation" network to distinguish the mobility patterns of an ego's social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual's mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego's predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego's predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent.
社会结构影响人类行为，包括他们的运动模式。事实上，个体运动的潜在信息可能存在于熟人及陌生人的移动模式中。我们开发了一种“同现”网络来区分个体社会关系的移动模式和那些与个体没有社会联系但在相似时间到达同一地点的人的移动模式。我们使用熵度量来分析和限制个体移动模式及其流向这两种关系的预测信息。虽然前者通常提供更多信息，可替代高达 94%的个体可预测性，但在未知同现者的聚合中也存在显著信息，包含高达 85%的个体预测信息。这种信息流引发了隐私问题：通过移动应用程序共享数据的个体可能会提供关于自己和其他数据不存在的人的可操作信息。
