Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond

TL;DR

This paper introduces an entailment-based measure for trajectory similarity that considers social and environmental facts, improving privacy protection and analysis of geographic data beyond traditional Euclidean approaches.

Contribution

It formalizes an entailment-based similarity measure for trajectories and demonstrates its application in evaluating privacy-preserving trajectory models.

Findings

Entailment-based similarity captures geographic and contextual nuances.

Applying the measure reveals risks of ignoring spatial structure.

The approach benefits location-privacy and trajectory analysis.

Abstract

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.