  Differentially private location trace synthesis (DPLTS) has recently emerged
as a solution to protect mobile users' privacy while enabling the analysis and
sharing of their location traces. A key challenge in DPLTS is to best preserve
the utility in location trace datasets, which is non-trivial considering the
high dimensionality, complexity and heterogeneity of datasets, as well as the
diverse types and notions of utility. In this paper, we present OptaTrace: a
utility-optimized and targeted approach to DPLTS. Given a real trace dataset D,
the differential privacy parameter epsilon controlling the strength of privacy
protection, and the utility/error metric Err of interest; OptaTrace uses
Bayesian optimization to optimize DPLTS such that the output error (measured in
terms of given metric Err) is minimized while epsilon-differential privacy is
satisfied. In addition, OptaTrace introduces a utility module that contains
several built-in error metrics for utility benchmarking and for choosing Err,
as well as a front-end web interface for accessible and interactive DPLTS
service. Experiments show that OptaTrace's optimized output can yield
substantial utility improvement and error reduction compared to previous work.
