Differentially Private Spatiotemporal Trajectory Synthesis with Retained Data Utility

TL;DR

This paper introduces DP-STTS, a differentially private method for synthesizing spatiotemporal trajectories that maintains high data utility by discretizing trajectories and employing a Markov process, balancing privacy and utility effectively.

Contribution

The paper presents a novel trajectory synthesizer combining discretization and Markov modeling under differential privacy, improving data utility in trajectory publishing.

Findings

DP-STTS achieves high data utility on real datasets.

The method effectively balances privacy and data utility.

Synthetic trajectories preserve key spatial-temporal features.

Abstract

Spatiotemporal trajectories collected from GPS-enabled devices are of vital importance to many applications, such as urban planning and traffic analysis. Due to the privacy leakage concerns, many privacy-preserving trajectory publishing methods have been proposed. However, most of them could not strike a good balance between privacy protection and good data utility. In this paper, we propose DP-STTS, a differentially private spatiotemporal trajectory synthesizer with high data utility, which employs a model composed of a start spatiotemporal cube distribution and a 1-order Markov process. Specially, DP-STTS firstly discretizes the raw spatiotemporal trajectories into neighboring cubes, such that the model size is limited and the model's tolerance for noise could be enhanced. Then, a Markov process is utilized for the next location point picking. After adding noise under differential privacy (DP) to the model, synthetic trajectories that preserve essential spatial and temporal characteristics of the real trajectories are generated from the noisy model. Experiments on one real-life dataset demonstrate that DP-STTS provides good data utility. Our code is available at https://github.com/Etherious72/DP-STTS.