Here Is Not There: Measuring Entailment-Based Trajectory Similarity for
  Location-Privacy Protection and Beyond
Zilong Liu Krzysztof Janowicz Kitty Currier Meilin Shi Jinmeng Rao Song Gao Ling Cai Anita Graser
While the paths humans take play out in social as well as physical space,
measures to describe and compare their trajectories are carried out in
abstract, typically Euclidean, space. When these measures are applied to
trajectories of actual individuals in an application area, alterations that are
inconsequential in abstract space may suddenly become problematic once overlaid
with geographic reality. In this work, we present a different view on
trajectory similarity by introducing a measure that utilizes logical
entailment. This is an inferential perspective that considers facts as triple
statements deduced from the social and environmental context in which the
travel takes place, and their practical implications. We suggest a
formalization of entailment-based trajectory similarity, measured as the
overlapping proportion of facts, which are spatial relation statements in our
case study. With the proposed measure, we evaluate LSTM-TrajGAN, a
privacy-preserving trajectory-generation model. The entailment-based model
evaluation reveals potential consequences of disregarding the rich structure of
geographic space (e.g., miscalculated insurance risk due to regional shifts in
our toy example). Our work highlights the advantage of applying logical
entailment to trajectory-similarity reasoning for location-privacy protection
and beyond.