Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy. (arXiv:2201.01139v1 [cs.LG])

Location data collected from mobile devices represent mobility behaviors at
individual and societal levels. These data have important applications ranging
from transportation planning to epidemic modeling. However, issues must be
overcome to best serve these use cases: The data often represent a limited
sample of the population and use of the data jeopardizes privacy.

To address these issues, we present and evaluate a system for generating
synthetic mobility data using a deep recurrent neural network (RNN) which is
trained on …

arxiv data privacy