Vehicle trajectory data de-identification method and system based on differential privacy

By dynamically and differentially allocating privacy budgets through geographic grid division and multidimensional feature analysis, the problem of privacy budget mismatch in existing technologies is solved, achieving efficient desensitization of vehicle trajectory data while balancing the protection of high-risk nodes and the data availability of low-risk nodes.

CN122197078BActive Publication Date: 2026-07-24SEEWORLD TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SEEWORLD TECH CO LTD
Filing Date
2026-05-13
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies cannot balance the key defense of high-risk nodes with the data availability of low-risk nodes in the desensitization of vehicle trajectory data, resulting in a mismatch of privacy budgets, failing to effectively protect highly sensitive stop points, and compromising the path continuity and macro-statistical availability of the trajectory.

Method used

By extracting the dwell coordinate set based on the geographic grid division, calculating multidimensional dwell features, and dynamically and differentially allocating privacy budgets according to sensitive categories, differential privacy noise is injected into the passing and dwell grid cells respectively to achieve differentiated noise addition processing.

Benefits of technology

By precisely matching protection strength with actual sensitivity requirements, the protection of highly sensitive areas is enhanced, while the data availability of low-sensitivity areas is preserved, thereby improving the overall security and macroscopic analysis availability of the desensitized trajectory.

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Abstract

The present application belongs to the technical field of data privacy protection, and particularly relates to a vehicle trajectory data desensitization method and system based on differential privacy, comprising the following steps: obtaining a single vehicle trajectory, mapping the single vehicle trajectory to a geographic grid, determining a passing grid unit and at least one stay grid unit, and extracting a stay coordinate set corresponding to the stay grid unit; determining the centroid coordinates of the stay coordinate set, respectively calculating the spatial scatter feature and the time offset feature of the stay coordinate set based on the centroid coordinates, combining the obtained historical access feature to generate a multi-dimensional stay feature; and determining the stay grid unit based on the multi-dimensional stay feature to obtain a sensitive class label. The present application accurately distinguishes real sensitive stays from passing areas, solves the privacy budget mismatch problem caused by equal noise addition, and realizes the perfect combination of strict protection of high-sensitive data and availability of macro trajectory analysis.
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