Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2023

Abstract

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world as well as synthetic data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the VLDB Endowment

Volume

16

Issue

8

First Page

1897

Last Page

1909

ISSN

2150-8097

Identifier

10.14778/3594512.3594520

Publisher

VLDB Endowment

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.14778/3594512.3594520

Download

Share

COinS