Utility-Optimized Synthesis of Differentially Private Location Traces

TL;DR

This paper introduces OptaTrace, a novel method for synthesizing differentially private location traces that optimizes utility using Bayesian techniques, significantly improving data usefulness while maintaining privacy.

Contribution

OptaTrace is the first utility-optimized, targeted approach to differential privacy in location trace synthesis, incorporating Bayesian optimization and a utility module.

Findings

Substantial utility improvement over previous methods

Error reduction achieved through Bayesian optimization

Interactive web interface for accessible DPLTS service

Abstract

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.