Skip to main content
SpringerLink
Log in

Deep Learning-based Privacy-preserving Publishing Method for Location Big Data in Vehicular Networks

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In contemporary times, there is an increasing integration of Location-Based Service (LBS) enabled smart devices into the fabric of individuals’ daily lives. The prevalent era of large-scale models predicting users’ historical location points poses a significant threat to user privacy. Simultaneously, conventional data release models exhibit suboptimal performance. This paper proposes a novel approach incorporating a deep learning prediction model and a location data release method called Hilbert-ConvLSTM, aimed at enhancing data availability while ensuring the privacy of user information. Firstly, leveraging the properties of the Hilbert curve, the predicted location point data is partitioned into multiple spatio-temporal structures. A sampling mechanism and exponential mechanism are employed for the selection of representative points within each location cluster. Subsequently, utilizing the “4V” characteristics of location point data, deep learning models are employed to extract spatio-temporal features, facilitating the prediction of location point data. Finally, in conjunction with the architecture derived from Hilbert curve partitioning, differential privacy budget allocation and Laplace noise addition are applied to achieve privacy protection in the statistical partitioned release of large-scale location data. Experimental analyses using real-world data validate the proposed method’s advantages in terms of data release usability and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Algorithm 2
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to the authors do not have the permission, but are available from the corresponding author on reasonable request.

References

  1. Qiu, M., Dai, W., & Vasilakos, A. (2019). Loop parallelism maximization for multimedia data processing in mobile vehicular clouds. IEEE Transactions on Cloud Computing, 7(1), 250–258.

    Article  Google Scholar 

  2. Qiu, M., Ming, Z., et al. (2015). Phase-change memory optimization for green cloud with genetic algorithm. IEEE Transactions on Computers, 64(12), 3528–3540.

    Article  MathSciNet  Google Scholar 

  3. Song, Y., Li, Y., et al. (2019). Retraining strategy-based domain adaption network for intelligent fault diagnosis. IEEE TII, 16(9), 6163–6171.

    Google Scholar 

  4. Qiu, H., Zheng, Q., et al. (2020). Toward secure and efficient deep learning inference in dependable IoT systems. IEEE Internet of Things Journal, 8(5), 3180–3188.

    Article  Google Scholar 

  5. Gai, K., Xu, K., et al. (2019). Fusion of cognitive wireless networks and edge computing. IEEE Wireless Communications, 26(3), 69–75.

    Article  Google Scholar 

  6. Wei, X., Guo, H., et al. (2021). Reliable data collection techniques in underwater wireless sensor networks: A survey. IEEE Communication Surveys & Tutorials, 24(1), 404–431.

    Article  MathSciNet  Google Scholar 

  7. Chen, S., Fu, A., Shen, J., Yu, S., Wang, H., & Sun, H. (2020). Rnn-dp: A new differential privacy scheme base on recurrent neural network for dynamic trajectory privacy protection. Journal of Network and Computer Applications, 168, 102736.

    Article  Google Scholar 

  8. Cheng, W., Wen, R., Huang, H., Miao, W., & Wang, C. (2022). Optdp: Towards optimal personalized trajectory differential privacy for trajectory data publishing. Neurocomputing, 472, 201–211.

    Article  Google Scholar 

  9. Li, Y., Yang, D., & Hu, X. (2020). A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data. Transportation Research Part C: Emerging Technologies, 115, 102634.

    Article  Google Scholar 

  10. Li, C., & Qiu, M. (2019). Reinforcement learning for cyber-physical systems: with cybersecurity case studies. Chapman and Hall/CRC.

  11. Ling, C., Jiang, J., Wang, J., et al. (2023). Deep graph representation learning and optimization for influence maximization. In: International Conference on Machine Learning (ICML), PMLR, pp. 21350–21361.

  12. Zhang, Y., Qiu, M., & Gao, H. (2023). Communication-efficient stochastic gradient descent ascent with momentum algorithms. In: IJCAI 2023.

  13. Zhao, X., Dong, Y., & Pi, D. (2019). Novel trajectory data publishing method under differential privacy. Expert Systems with Applications, 138, 112791.

    Article  Google Scholar 

  14. Rao, J., Gao, S., Kang, Y., et al. (2020). Lstm-trajgan: A deep learning approach to trajectory privacy protection. Preprint retrieved from http://arxiv.org/abs/2006.10521

  15. Ghane, S., Kulik, L., & Ramamohanarao, K. (2019). Tgm: A generative mechanism for publishing trajectories with differential privacy. IEEE Internet of Things Journal, 7(4), 2611–2621.

    Article  Google Scholar 

  16. Rao, J., Gao, S., & Zhu, S. (2023). CATS: Conditional adversarial trajectory synthesis for privacy-preserving trajectory data publication using deep learning approaches. International Journal of Geographical Information Science, 37(12), 2538–2574.

    Article  Google Scholar 

  17. Wang, W., Wang, Y., Duan, P., et al. (2022). A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing. IEEE Transactions on Mobile Computing.

  18. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557–570.

    Article  MathSciNet  Google Scholar 

  19. Gruteser, M., & Grunwald, D. (2003). Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42.

  20. Gursoy, M. E., Liu, L., Truex, S., & Yu, L. (2018). Differentially private and utility preserving publication of trajectory data. IEEE Transactions on Mobile Computing, 18(10), 2315–2329.

    Article  Google Scholar 

  21. Qiu, M., Gai, K., & Xiong, Z. (2018). Privacy-preserving wireless communications using bipartite matching in social big data. FGCS, 87, 772–781.

    Article  Google Scholar 

  22. Qiu, M., Gai, K., Zhao, H., et al. (2018). Privacy-preserving smart data storage for financial industry in cloud computing. Concurrency and Computation: Practice and Experience, 30(5), e4278.

    Article  Google Scholar 

  23. Gai, K., Qiu, M., & Zhao, H. (2021). Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Transactions on Big Data, 7(4), 678–688.

    Google Scholar 

  24. Zeng, Y., Pan, M., Just, H. A., et al. (2023). Narcissus: A practical clean-label backdoor attack with limited information. In: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, pp. 771–785.

  25. Qiu, M., & Qiu, H. (2020). Review on image processing based adversarial example defenses in computer vision. In: 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), IEEE, pp. 94–99.

  26. Zeng, Y., Qiu, H., Memmi. G., et al. (2020). A data augmentation-based defense method against adversarial attacks in neural networks. In: Algorithms and Architectures for Parallel Processing: 20th International Conference, ICA3PP 2020, New York City, NY, USA, October 2–4, 2020, Proceedings, Part II 20. Springer International Publishing, pp. 274–289.

  27. Qardaji, W., Yang, W., & Li, N. (2013). Differentially private grids for geospatial data. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE, pp. 757–768.

  28. Xiong, P., Zhang, L., & Zhu, T. (2017). Reward-based spatial crowdsourcing with differential privacy preservation. Enterprise Information Systems, 11(10), 1500–1517.

    Article  Google Scholar 

  29. Wang, J., Zhu, R., Liu, S., & Cai, Z. (2018). Node location privacy protection based on differentially private grids in industrial wireless sensor networks. Sensors, 18(2), 410.

    Article  Google Scholar 

  30. Cui, J., Ding, Z., Deng, Y., Nallanathan, A., & Hanzo, L. (2020). Adaptive uav-trajectory optimization under quality of service constraints: A model-free solution. IEEE Access, 8, 112253–112265.

    Article  Google Scholar 

  31. Jin, Y., Yu, R., Si, H., Bian, Y., Qiao, X., Ji, L., Liu, Q., Wang, W., Yu, J., Li, Y., et al. (2022). Effects of social support on frailty trajectory classes among community-dwelling older adults: the mediating role of depressive symptoms and physical activity. Geriatric Nursing, 45, 39–46.

  32. Zhao, X., Pi, D., & Chen, J. (2020). Novel trajectory privacy-preserving method based on clustering using differential privacy. Expert Systems with Applications, 149, 113241.

  33. Zhao, Y., Wu, W., & Di, C. (2021). Release of trajectory data based on space segmentation using differential privacy. In: 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, pp. 1246–1253.

  34. Wu, Y., Cao, X., & An, Z. (2020). A spatiotemporal trajectory data index based on the Hilbert curve code. IOP Conference Series: Earth and Environmental Science, 502(1), 012005.

    Google Scholar 

  35. Yuan, J., Zheng, Y., Zhang, C., et al. (2010). T-drive: Driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108.

  36. Chen, X., Xu, J., Zhou, R., Chen, W., Fang, J., & Liu, C. (2021). Trajvae: A variational autoencoder model for trajectory generation. Neurocomputing, 428, 332–339.

    Article  Google Scholar 

  37. Kim, J. W., & Jang, B. (2022). Deep learning-based privacy-preserving framework for synthetic trajectory generation. Journal of Network and Computer Applications, 206, 103459.

    Article  Google Scholar 

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. China Industrial Control Systems Cyber Emergency Response Team, 35 Lugu Rd, Shijingshan District, Beijing, 100040, China

    Caiyun Liu, Jun Li & Yan Sun

Authors
  1. Caiyun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Sun.

Ethics declarations

Ethics Approval

Not Applicable.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, C., Li, J. & Sun, Y. Deep Learning-based Privacy-preserving Publishing Method for Location Big Data in Vehicular Networks. J Sign Process Syst (2024). https://doi.org/10.1007/s11265-024-01912-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11265-024-01912-z

Keywords

Search

Navigation