论文标题
通过位置语义对不完善的知识进行建模，以实现轨迹数据中的现实隐私风险估算
Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data
论文作者
论文摘要
车辆和人员的移动性模式为基于位置的服务（例如车队优化和交通流量分析）提供了强大的数据源。基于位置的服务提供商必须平衡他们从轨迹数据中提取的价值，并保护这些轨迹背后的个人的隐私。达到此目标需要准确衡量效用和隐私的价值。当前的测量方法假设具有完美知识的对手，从而高估了隐私风险。为了解决这个问题，我们介绍了一个对目标的模型，对目标的知识不完美。该模型基于等价区域，具有语义含义的时空区域，例如目标的房屋的大小和准确性决定了对手的技能。然后，我们从等价区域的定义中得出了K-匿名，L多样性和T-Closenes的标准隐私指标。这些指标都可以在任何数据集上计算，而不论是否应用于哪种匿名化。这项工作与所有想要管理隐私风险并优化其服务的隐私风险和优化的轨迹数据处理器的服务提供商具有很高的相关性。
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.
