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Differentially private location trace synthesis (DPLTS) has recently emergedas a solution to protect mobile users' privacy while enabling the analysis andsharing of their location traces. A key challenge in DPLTS is to best preservethe utility in location trace datasets, which is non-trivial considering thehigh dimensionality, complexity and heterogeneity of datasets, as well as thediverse types and notions of utility. In this paper, we present OptaTrace: autility-optimized and targeted approach to DPLTS. Given a real trace dataset D,the differential privacy parameter epsilon controlling the strength of privacyprotection, and the utility/error metric Err of interest; OptaTrace usesBayesian optimization to optimize DPLTS such that the output error (measured interms of given metric Err) is minimized while epsilon-differential privacy issatisfied. In addition, OptaTrace introduces a utility module that containsseveral built-in error metrics for utility benchmarking and for choosing Err,as well as a front-end web interface for accessible and interactive DPLTSservice. Experiments show that OptaTrace's optimized output can yieldsubstantial utility improvement and error reduction compared to previous work.

Utility-Optimized Synthesis of Differentially Private Location Traces