Skip to main content
SpringerLink
Log in

User Grouping Privacy-Preserving Strategy Based on Edge Computing for Mobile Crowdsensing

  • Conference paper
  • First Online:
Security and Privacy in Digital Economy (SPDE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1268))

Included in the following conference series:

  • 1705 Accesses

Abstract

At present, the data information is uploaded to the platform by a large number of users in mobile crowdsensing, which can be processed in the sensing platform. As a result, the sensing platform not only a target for attackers, but also greatly increases time delay and high bandwidth costs. To solve this problem, a user clustering privacy-preserving scheme based on edge servers (UPPE) is proposed. Firstly, the task requester sends the tasks to the platform, publishes the task after sensing the platform’s scalar task attributes, and the sensing user uploads the user task request to the edge server. Then, the edge server utilizes the clustering method to hide the information related users, taking the edge server as a clustering to sign the users and upload it to the platform. Finally, the platform randomly matches the users and selects the user set in the clustering. The edge server notifies the user to upload the sensing data after signature verification. The simulation results show that the proposed mechanism verifies the user availability to upload data and protect the identity of user.

This work was supported by the National Key Research and Development Project of China under Grant 2018YFB2100200.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yang, D., et al.: Incentive mechanisms for crowdsensing: crowdsourcing with smartphones. IEEE/ACM Trans. Networking 24(3), 1732–1744 (2015)

    Article  Google Scholar 

  2. Hicks, J., et al.: AndWellness: an open mobile system for activity and experience sampling. Wireless Health 2010, 34–43 (2010)

    Google Scholar 

  3. Ni, J., et al.: Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Commun. Mag. 55(6), 146–152 (2017)

    Article  Google Scholar 

  4. Zhao, Y., et al.: Greendrive: a smartphone-based intelligent speed adaptation system with real-time traffic signal prediction. In: 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS). IEEE (2017)

    Google Scholar 

  5. Mun, M., et al.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68 (2009)

    Google Scholar 

  6. Marjanovic, M., Aleksandar, A., Zarko, I.P.: Edge computing architecture for mobile crowdsensing. IEEE Access 6, 10662–10674 (2018)

    Article  Google Scholar 

  7. Li, Y., et al.: Privacy-preserving location proof for securing large-scale database-driven cognitive radio networks. IEEE Internet Things J. 3(4), 563–571 (2015)

    Article  MathSciNet  Google Scholar 

  8. Wang, X.O., et al.: Enabling reputation and trust in privacy-preserving mobile sensing. IEEE Trans. Mobile Comput. 13(12), 2777–2790 (2013)

    Article  Google Scholar 

  9. Fan, J., Li, Q., Cao, G.: Privacy-aware and trustworthy data aggregation in mobile sensing. In: 2015 IEEE Conference on Communications and Network Security (CNS), pp. 31–39. IEEE (2015)

    Google Scholar 

  10. Zhang, Y., Chen, Q., Zhong, S.: Efficient and privacy-preserving min and \( k \) th min computations in mobile sensing systems. IEEE Trans. Dependable Secure Comput. 14(1), 9–21 (2015)

    Google Scholar 

  11. Li, Q., Cao, G., La Porta, T.F.: Efficient and privacy-aware data aggregation in mobile sensing. IEEE Trans. Dependable Secure Comput. 11(2), 115–129 (2013)

    Article  Google Scholar 

  12. Yang, B., Dapeng, W., Wang, R.: CUE: an intelligent edge computing framework. IEEE Network 33(3), 18–25 (2019)

    Article  Google Scholar 

  13. Shi, W., et al.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  14. Li, T., et al.: Participant grouping for privacy preservation in mobile crowdsensing over hierarchical edge clouds. In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). IEEE (2018)

    Google Scholar 

  15. Zheng, Yu., Xie, X., Ma, W.-Y.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)

    Google Scholar 

  16. Song, Z., Li, Z., Chen, X.: Local differential privacy preserving mechanism for multi-attribute data in mobile crowdsensing with edge computing. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE (2019)

    Google Scholar 

  17. Ren, X., et al.: LoPub: high-dimensional crowdsourced data publication with local differential privacy. IEEE Trans. Inf. Forensics Secur. 13(9), 2151–2166 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chongqing Academy of Information and Communications Technology, Chongqing, 401336, China

    Peng Yang & Yan Zhang

  2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Peng Yang, Qiming Wu, Dapeng Wu & Zhidu Li

  3. Key Laboratory of Optical Communication and Network in Chongqing, Chongqing, 400065, China

    Peng Yang, Qiming Wu, Dapeng Wu & Zhidu Li

  4. Key Laboratory of Ubiquitous Sensing and Networking in Chongqing, Chongqing, 400065, China

    Peng Yang, Qiming Wu, Dapeng Wu & Zhidu Li

Authors
  1. Peng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dapeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhidu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Yang .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shui Yu

  2. IBM Zurich Research Laboratory, Zurich, Switzerland

    Peter Mueller

  3. Ningbo University, Ningbo, China

    Jiangbo Qian

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, P., Zhang, Y., Wu, Q., Wu, D., Li, Z. (2020). User Grouping Privacy-Preserving Strategy Based on Edge Computing for Mobile Crowdsensing. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_50

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-9129-7_50

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9128-0

  • Online ISBN: 978-981-15-9129-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation