Skip to main content
Log in

A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the process of crowdsensing, tasks allocation is an important part of the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may be aware of the location of each other, and then threaten their privacy. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the process of task matching by applicants and publishers is also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publishers can be preserved. Finally, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. In the last part of this paper, four types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of our proposed scheme.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Zhang, L., Liu, D., Chen, M., Li, H., Wang, C., Zhang, Y., & Du, Y. (2021). A user collaboration privacy protection scheme with threshold scheme and smart contract. Information Sciences, 560, 183–201.

    Article  MathSciNet  Google Scholar 

  2. Huang, P., Zhang, X. N., Guo, L. K., & Li, M. (2021). Incentivizing crowdsensing-based noise monitoring with differentially-private locations. IEEE Transactions on Mobile Computing, 20(2), 519–532.

    Article  Google Scholar 

  3. Zhang, L., Chen, M., Liu, D., He, L., Wang, C., Sun, Y., & Wang, B. (2020). A ε-sensitive indistinguishable scheme for privacy preserving. Wireless Networks, 26(07), 5013–5033.

    Article  Google Scholar 

  4. Vergara-Laurens, I. J., Jaimes, L. G., & Labrador, M. A. (2017). Privacy-preserving mechanisms for crowdsensing: Survey and research challenges. IEEE Internet of Things Journal, 4(4), 855–869.

    Article  Google Scholar 

  5. Wang, Y., Cai, Z., Tong, X., Yang, G., & Yin, G. (2018). Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. Computer Networks, 135, 32–43.

    Article  Google Scholar 

  6. Zhang, L., Li, J., Yang, S., & Wang, B. (2017). Privacy preserving in cloud environment for obstructed shortest path query. Wireless Personal Communications, 96(2), 2305–2322.

    Article  Google Scholar 

  7. Zhao, C., Yang, S. S., & McCann, J. A. (2021). On the data quality in privacy-preserving mobile crowdsensing systems with untruthful reporting. IEEE Transactions on Mobile Computing, 20(2), 647–661.

    Article  Google Scholar 

  8. Sadhu, V., Zonouz, S., Sritapan, V., & Pompili, D. (2021). CollabLoc: Privacy-preserving multi-modal collaborative mobile phone localization. IEEE Transactions on Mobile Computing, 20(1), 104–116.

    Article  Google Scholar 

  9. Shu, J., Jia, X., Kan, Y., & Hua, W. (2018). Privacy-preserving task recommendation services for crowdsourcing. IEEE Transactions on Services Computing, 99, 1–1.

    Google Scholar 

  10. Zhang, Y. H., Li, M., Yang, D. J., Tang, J., Xue, G. L., & Xu, J. (2020). Tradeoff between location quality and privacy in crowdsensing: An optimization perspective. IEEE Internet of Things Journal, 7(4), 3535–3544.

    Article  Google Scholar 

  11. Zhang, L., Yang, S., Li, J., & Yu, L. (2018). A particle swarm optimization clustering-based attribute generalization privacy protection scheme. Journal of Circuits, Systems and Computers, 27(11), 641–654.

    Article  Google Scholar 

  12. Wei, J., Lin, Y., Yao, X., & Zhang, J. (2019). Differential privacy-based location protection in spatial crowdsourcing.

  13. Luo, G. C., Yan, K., Zheng, X., Tian, L., & Cai, Z. P. (2020). Preserving adjustable path privacy for task acquisition in mobile crowdsensing Systems. Information Sciences, 527, 602–619.

    Article  MathSciNet  Google Scholar 

  14. Xu, J., Cui, B. J., Shi, R. S., & Feng, Q. L. (2020). Outsourced privacy-aware task allocation with flexible expressions in crowdsourcing. Future Generation Computer Systems-the International Journal of Escience, 112, 383–393.

    Article  Google Scholar 

  15. Zou, S. H., Xi, J. W., Wang, H. G., & Xu, G. A. (2020). CrowdBLPS: A blockchain-based location-privacy-preserving mobile crowdsensing system. IEEE Transactions on Industrial Informatics, 16(6), 4206–4218.

    Article  Google Scholar 

  16. Zhu, X. J., Ayday, E., & Vitenberg, R. (2021). A privacy-preserving framework for outsourcing location-based services to the cloud. IEEE Transactions on Dependable and Secure Computing, 18(1), 384–399.

    Article  Google Scholar 

  17. Yang, M. M., Zhu, T. Q., Xiang, Y., & Zhou, W. L. (2018). Density-based location preservation for mobile crowdsensing with differential privacy. IEEE Access, 6, 14779–14789.

    Article  Google Scholar 

  18. Yang, M., Zhu, T., Liang, K., & Zhou, W. (2019). A blockchain-based location privacy-preserving crowdsensing system. Future Generation Computer Systems-the International Journal of Escience, 94, 408–418.

    Article  Google Scholar 

  19. He, Y. Y., Ni, J. B., Niu, B., Li, F. H., & Shen, X. M. (2020). Privbus: A privacy-enhanced crowdsourced bus service via fog computing. Journal of Parallel and Distributed Computing, 135, 156–168.

    Article  Google Scholar 

  20. Wang, L. Y., Zhang, D. Q., Yang, D. Q., Lim, B. Y., Han, X., & Ma, X. J. (2020). Sparse mobile crowdsensing with differential and distortion location privacy. IEEE Transactions on Information Forensics and Security, 15, 2735–2749.

    Article  Google Scholar 

  21. Yuan, D., Li, Q., Li, G. L., Wang, Q., & Ren, K. (2020). PriRadar: A privacy-preserving framework for spatial crowdsourcing. IEEE Transactions on Information Forensics and Security, 15, 299–314.

    Article  Google Scholar 

  22. Zhao, Z. J., Ying, Z. B., Yang, Z., Liu, X. M., & Ma, J. F. (2020). Recommendation of platoon members by combining the blockchain and vehicular social network. Journal of Xidian University, 47(5), 122–129.

    Google Scholar 

  23. Wang, L. Y., Yang, D. Q., Han, X., Wang, T. B., Zhang, D. Q., Ma, X. J., & Acm. (2017). Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation.

  24. Wang, J., Wang, Y., Zhao, G., & Zhao, Z. (2019). Location protection method for mobile crowd sensing based on local differential privacy preference. Peer-to-Peer Networking and Applications, 12(5), 1097–1109.

    Article  Google Scholar 

  25. Ni, J. B., Zhang, K., Xia, Q., Lin, X. D., & Shen, X. M. (2020). Enabling strong privacy preservation and accurate task allocation for mobile crowdsensing. IEEE Transactions on Mobile Computing, 19(6), 1317–1331.

    Article  Google Scholar 

  26. Niu, X., Huang, H. Y., & Li, Y. T. (2020). A real-time data collection mechanism with trajectory privacy in mobile crowd-sensing. IEEE Communications Letters, 24(10), 2114–2118.

    Article  Google Scholar 

  27. Tong, L., Zhu, Y., Wen, T., & Yu, J. (2018). Location privacy-preserving method for auction-based incentive mechanisms in mobile crowd sensing. Computer Journal, 61(6), 937–948.

    Article  Google Scholar 

  28. Tao, D., Wu, T. Y., Zhu, S. J., & Guizani, M. (2020). Privacy protection-based incentive mechanism for mobile crowdsensing. Computer Communications, 156, 201–210.

    Article  Google Scholar 

  29. Sun, Z., Wang, Y., Cai, Z., Liu, T., Tong, X., & Jiang, N. (2021). A two-stage privacy protection mechanism based on blockchain in mobile crowdsourcing. International Journal of Intelligent Systems, 36(5), 2058–2080.

    Article  Google Scholar 

  30. Zhang, J. W., Yang, F., Ma, Z., Wang, Z. Z., Liu, X. M., & Ma, J. F. (2021). A decentralized location privacy-preserving spatial crowdsourcing for internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2299–2313.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to present our thanks to anonymous reviewers for their helpful suggestions. This work was supported by the Natural Science Foundation of Heilongjiang Province of China under Grant LH2020F050, National Natural Science Foundation of China (No. 61872204). Science Research project of Basic scientific research business expenses in Heilongjiang Provincial colleges and universities of China (No. 135309453).

Funding

The Natural Science Foundation of Heilongjiang Province of China under Grant LH2020F050, National Natural Science Foundation of China (No. 61872204). Science Research Project of Basic Scientific Research Business Expenses in Heilongjiang Provincial Colleges and Universities of China (No. 135309453).

Author information

Authors and Affiliations

Authors

Contributions

XdZ gave the idea, LZ and BW did the experiments, XdZ and QY interpreted the results, XdZ wrote the paper.

Corresponding author

Correspondence to Xiaodong Zheng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, X., Yuan, Q., Wang, B. et al. A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation. Wireless Pers Commun 126, 719–740 (2022). https://doi.org/10.1007/s11277-022-09767-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09767-y

Keywords

Navigation