Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target's home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

翻译：车辆和人员的流动模式为基于地点的服务提供了强有力的数据来源,如车队优化和交通流量分析。基于地点的服务提供者必须平衡从轨迹数据中提取的价值和保护这些轨迹背后的个人隐私。实现这一目标需要准确衡量效用和隐私的价值。目前的衡量方法假定对手是完全知情的,从而高估了隐私风险。为了解决这一问题,我们采用了对目标有不完全了解的对立模型。模型基于等效领域,具有语义意义的时空区域,例如目标的家,其大小和准确性决定对手的技巧。然后,我们从对等领域的定义中得出k-匿名、l-多样性和t-close等标准隐私衡量标准。这些衡量标准可以根据任何数据集进行计算,而不论是否和对它应用了何种名称化。这项工作对于所有服务供应商都具有高度相关性,因为他们想要管理隐私风险并优化其服务的隐私与实用性交易。