TraBiMap: Reducing Privacy Concerns in Trajectory Analysis with Randomized Data Representations

Personal trajectory data, increasingly acquired by more and more GNSS-enabled devices, is currently underutilized as privacy concerns prohibit its comprehensive excavation to improve reaction and planning opportunities for communities and individuals. However, due to their distinctive data structure, existing data anonymization methods are difficult to apply directly to trajectories. Therefore, inspired by recent probabilistic representations of geographic information, we present TraBiMaps, a randomized data structure for trajectories based on Bloom Filters using cryptographic hash functions that can efficiently store and evaluate rasterized trajectories with a high level of individual privacy. We further provide a preliminary privacy analysis of TraBiMap and pose additional research questions in this field.