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<p>With the rapid advancement of autonomous driving technology, precise user trajectory data has become critical for vehicles. Current privacy-preserving methodologies predominantly focus on spatial feature extraction while insufficiently addressing temporal dimension vulnerabilities, a security-critical oversight that disrupts the privacy-utility trade-off. We propose a Dual-Layer Temporal Privacy Protection Model (DLTPPM) featuring: (1) A modified Trajectory Chain Variational Autoencoder (TCVAE) combining Long Short-Term Memory (LSTM) with enhanced variational inference for expressive spatiotemporal pattern learning; (2) A Random Forest-Laplace Time Protector (RF-LTP) applying context-aware differential privacy via adaptive temporal noise injection. Experimental validation confirms DLTPPM’s capability to simultaneously enhance privacy protection and data utility, demonstrating significant balanced improvement over conventional approaches.</p>

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DLTPPM: a dual-layer model for trajectory privacy protection in autonomous driving

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Fanshu Shang,
  • Yong Wang,
  • Jing Yang,
  • Shuo Wang,
  • Jiaqi Liu

摘要

With the rapid advancement of autonomous driving technology, precise user trajectory data has become critical for vehicles. Current privacy-preserving methodologies predominantly focus on spatial feature extraction while insufficiently addressing temporal dimension vulnerabilities, a security-critical oversight that disrupts the privacy-utility trade-off. We propose a Dual-Layer Temporal Privacy Protection Model (DLTPPM) featuring: (1) A modified Trajectory Chain Variational Autoencoder (TCVAE) combining Long Short-Term Memory (LSTM) with enhanced variational inference for expressive spatiotemporal pattern learning; (2) A Random Forest-Laplace Time Protector (RF-LTP) applying context-aware differential privacy via adaptive temporal noise injection. Experimental validation confirms DLTPPM’s capability to simultaneously enhance privacy protection and data utility, demonstrating significant balanced improvement over conventional approaches.