Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data
This work is highly relevant for location-based service providers processing trajectory data, enabling them to more realistically manage privacy risks and optimize the privacy vs. utility trade-off.
This paper introduces a model for adversaries with imperfect knowledge to estimate privacy risks in trajectory data, addressing the overestimation of risk by current methods assuming perfect knowledge. The model uses 'equivalence areas' to define adversary skill, from which standard privacy metrics like k-anonymity, l-diversity, and t-closeness are derived and can be computed on any dataset.
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.