PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model

2022/10/02 by Zhikun Zhang, Wang, Haiming, Tianhao Wang +10 · 4 citations
Computer Science · Engineering · Social Sciences · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Privacy-Preserving Technologies in Data #Vehicular Ad Hoc Networks (VANETs)

paper · pdf · doi:10.48550/arxiv.2210.00581

openalex publication_date 2022/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Publishing trajectory data (individual's movement information) is very useful, but it also raises privacy concerns. To handle the privacy concern, in this paper, we apply differential privacy, the standard technique for data privacy, together with Markov chain model, to generate synthetic trajectories. We notice that existing studies all use Markov chain model and thus propose a framework to analyze the usage of the Markov chain model in this problem. Based on the analysis, we come up with an effective algorithm PrivTrace that uses the first-order and second-order Markov model adaptively. We evaluate PrivTrace and existing methods on synthetic and real-world datasets to demonstrate the superiority of our method.

Cited by

• PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
• What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
• Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
• Bilateral Differentially Private Vertical Federated Boosted Decision Trees

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