Generative Models for Simulating Mobility Trajectories
This addresses the need for privacy-preserving mobility datasets for geographic information systems, but it is incremental as it benchmarks existing methods without introducing a new paradigm.
The paper tackled the problem of generating synthetic mobility trajectories that preserve privacy while maintaining realistic characteristics, by benchmarking RNNs, GANs, and nonparametric copulas, resulting in evaluations of geographic similarity, circadian rhythms, and privacy tradeoffs.
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is vulnerable to membership inference attacks. Current synthetic mobility dataset generators attempt to superficially match a priori modeled mobility characteristics which do not accurately reflect the real-world characteristics. Modeling human mobility to generate synthetic yet semantically and statistically realistic trajectories is therefore crucial for publishing trajectory datasets having satisfactory utility level while preserving user privacy. Specifically, long-range dependencies inherent to human mobility are challenging to capture with both discriminative and generative models. In this paper, we benchmark the performance of recurrent neural architectures (RNNs), generative adversarial networks (GANs) and nonparametric copulas to generate synthetic mobility traces. We evaluate the generated trajectories with respect to their geographic and semantic similarity, circadian rhythms, long-range dependencies, training and generation time. We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.