Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{free} services and then monetize it for advertising and marketing purposes. While major tech companies only sell aggregate behaviors for marketing purposes; data aggregators and data brokers offer access to individual location data. Some data brokers and aggregators have certain rules in place to preserve privacy; and the FTC has also started to vigorously regulate consumer privacy for location data. In this paper, we present an in-depth exploration of U.S. privacy perceptions with respect to specific location features derivable from data made available by location data brokers and aggregators. These results can provide policy implications that could assist organizations like the FTC in defining clear access rules. Using a factorial vignette survey, we collected responses from 1,405 participants to evaluate their level of comfort with sharing different types of location features, including individual trajectory data and visits to points of interest, available for purchase from data brokers worldwide. Our results show that trajectory-related features are associated with higher privacy concerns, that some data broker based obfuscation practices increase levels of comfort, and that race, ethnicity and education have an effect on data sharing privacy perceptions. We also model the privacy perceptions of people as a predictive task with F1 score \textbf{0.6}.

翻译：随着智能手机的普及，位置数据收集已变得无处不在。智能手机应用通常通过提供免费服务来收集用户的精确位置数据，随后将其用于广告和营销目的以实现货币化。尽管大型科技公司仅为营销目的出售聚合行为数据；但数据聚合商和数据经纪商则提供对个人位置数据的访问权限。部分数据经纪商和聚合商制定了特定规则以保护隐私；美国联邦贸易委员会（FTC）也已开始大力监管位置数据的消费者隐私。本文深入探讨了美国公众对位置数据经纪商和聚合商所提供数据中可推导出的具体位置特征的隐私认知。这些研究结果可为政策制定提供参考，协助FTC等机构明确数据访问规则。通过采用因子情景调查法，我们收集了1,405名参与者的反馈，评估其对分享不同类型位置特征的接受程度，包括可从全球数据经纪商处购买的个人轨迹数据和兴趣点访问记录。研究结果表明：轨迹相关特征会引发更高的隐私担忧；某些基于数据经纪商的模糊化处理能提升接受度；种族、民族和教育背景会影响数据共享的隐私认知。我们还将公众隐私认知建模为预测任务，取得了F1分数为0.6的预测性能。