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PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model

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conference contribution
posted on 2023-11-29, 18:24 authored by Haiming Wang, Zhikun ZhangZhikun Zhang, Tianhao Wang, Shibo He, Michael BackesMichael Backes, Jiming Chen, Yang ZhangYang Zhang
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.

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Preferred Citation

Haiming Wang, Zhikun Zhang, Tianhao Wang, Shibo He, Michael Backes, Jiming Chen and Yang Zhang. PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model. In: Usenix Security Symposium (USENIX-Security). 2023.

Primary Research Area

  • Algorithmic Foundations and Cryptography

Name of Conference

Usenix Security Symposium (USENIX-Security)

Legacy Posted Date

2022-11-20

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_3877, title = "PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model", author = "Wang, Haiming and Zhang, Zhikun and Wang, Tianhao and He, Shibo and Backes, Michael and Chen, Jiming and Zhang, Yang", booktitle="{Usenix Security Symposium (USENIX-Security)}", year="2023", }

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