Trajectory Privacy Protection Mechanism based on Social Attributes

TL;DR

This paper introduces a novel trajectory privacy protection mechanism that incorporates social attributes, addressing the limitations of existing methods which only consider spatial and temporal data, by proposing a multi-dimensional anonymization approach.

Contribution

It develops a social attribute-aware trajectory k-anonymity algorithm and a multi-dimensional privacy-preserving model integrating space, time, social, and semantic data.

Findings

The social privacy attack model reveals how social attributes can lead to privacy leaks.

The proposed algorithms enhance trajectory privacy protection against social attribute-based attacks.

Experimental results demonstrate improved privacy preservation with the new multi-dimensional approach.

Abstract

The current trajectory privacy protection technology only considers the temporal and spatial attributes of trajectory data, but ignores the social attributes. However, there is an intrinsic relationship between social attributes and human activity trajectories, which brings new challenges to trajectory privacy protection, making existing trajectory privacy protection technologies unable to resist trajectory privacy attacks based on social attributes. To this end, this paper first studies the social privacy attack in the trajectory data, builds a social privacy attack model based on the fusion of "space-time" features, and reveals the internal impact of the spatial and temporal features in the trajectory data on social privacy leaks. -Anonymous algorithm and trajectory release privacy protection provide theoretical support. On this basis, integrate social attributes into trajectory privacy protection technology, design trajectory k-anonymity algorithm based on "space-time-social" three-dimensional mobile model, and construct trajectory data based on "space-time-social-semantic" multi-dimensional correlation Publish privacy-preserving models.