Spatio-temporal Trajectory Dataset Privacy Based on Network Traffic Control

TL;DR

This paper proposes a hybrid differential privacy method called APTB for protecting user trajectory data in mobile social networks, addressing high dimensionality and sparsity issues in data publishing.

Contribution

It introduces a novel hybrid publishing approach using similarity aggregation and prefix trees to enhance privacy in spatiotemporal trajectory data.

Findings

Effective privacy protection for high-dimensional trajectory data

Improved data utility through the APTB method

Addresses sparsity issues in trajectory data publishing

Abstract

Collection of user's location and trajectory information that contains rich personal privacy in mobile social networks has become easier for attackers. Network traffic control is an important network system which can solve some security and privacy problems. In this paper, we consider a network traffic control system as a trusted third party and use differential privacy for protecting more personal trajectory data. We studied the influence of the high dimensionality and sparsity of trajectory data sets based on the availability of the published results. Based on similarity point aggregation reconstruction ideas and a prefix tree model, we proposed a hybrid publishing method of differential privacy spatiotemporal trajectory data sets APTB.