LDPTrace: Locally Differentially Private Trajectory Synthesis
Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng,, Yunjun Gao

TL;DR
LDPTrace is a new framework that synthesizes realistic trajectory data under local differential privacy, addressing utility, computational, and security challenges in privacy-preserving data sharing.
Contribution
It introduces a novel trajectory synthesis method considering user movement patterns and a grid granularity selection technique, improving utility and efficiency under local differential privacy.
Findings
Achieves high utility in synthetic trajectories with minimal computational overhead
Effectively resists privacy attacks in experiments
Outperforms existing methods in real-world data utility metrics
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, privay 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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Human Mobility and Location-Based Analysis · Vehicular Ad Hoc Networks (VANETs)
