Releasing Differentially Private Trajectories with Optimized Data Utility

Abstract

The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Fur-thermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.