Privacy-Preserving Task Allocation for Edge Computing Enhanced Mobile Crowdsensing

  • Yujia Hu
  • Hang ShenEmail author
  • Guangwei Bai
  • Tianjing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)


In traditional mobile crowdsensing (MCS) applications, the crowdsensing server (CS-server) need mobile users’ precise locations for optimal task allocation, which raises privacy concerns. This work proposes a framework P2TA to optimize task acceptance rate while protecting users’ privacy. Specifically, edge nodes are introduced as an anonymous server and a task allocation agent to prevent CS-server from directly obtaining user data and dispersing privacy risks. On this basis, a genetic algorithm that performed on edge nodes is designed to choose an initial obfuscation strategy. Furthermore, a privacy game model is used to optimize user/adversary objectives against each other to obtain a final obfuscation strategy which can be immune to posterior inference. Finally, edge nodes take user acceptance rate and task allocation rate into account comprehensively, focusing on maximizing the expected accepted task number under the constraint of differential privacy and distortion privacy. The effectiveness and superiority of P2TA to the exiting MCS task allocation schemes are validated via extensive simulations on the synthetic data, as well as the measured data collected by ourselves.


Mobile crowdsensing Edge computing Privacy preserving 


  1. 1.
    Alsheikh, M.A., Jiao, Y.: The accuracy-privacy trade-off of mobile crowdsensing. IEEE Commun. Mag. 55(6), 132–139 (2017)CrossRefGoogle Scholar
  2. 2.
    Ma, H., Zhao, D.: Opportunities in mobile crowd sensing. IEEE Commun. Mag. 52(8), 29–35 (2014)CrossRefGoogle Scholar
  3. 3.
    Yang, D., Xue, G., Fang, X.: Incentive mechanisms for crowdsensing: crowdsourcing with smartphones. IEEE/ACM Trans. Netw. 24(3), 1732–1744 (2016)CrossRefGoogle Scholar
  4. 4.
    Shi, W., Cao, J., Zhang, Q.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  5. 5.
    Guo, B., Liu, Y., Wang, L., Li, V.O.K.: Task allocation in spatial crowdsourcing: current state and future directions. IEEE Internet Things J. PP(99), 1 (2018)Google Scholar
  6. 6.
    Guo, B., Liu, Y., Wu, W.: ActiveCrowd: a framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Trans. Hum.-Mach. Syst. 47(3), 392–403 (2017)CrossRefGoogle Scholar
  7. 7.
    Wang, L., Zhang, D., Yang, D.: Differential location privacy for sparse mobile crowdsensing. In: Proceedings of IEEE ICDM (2017)Google Scholar
  8. 8.
    He, S., Shin, D.H., Zhang, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: Proceedings of IEEE INFOCOM, pp. 745–753 (2014)Google Scholar
  9. 9.
    Shokri, R., Theodorakopoulos, G., Troncoso, C.: Protecting location privacy: optimal strategy against localization attacks. In: Proceedings of ACM CCS, pp. 617–627 (2016)Google Scholar
  10. 10.
    Brown, J.W.S., Ohrimenko, O.: Haze: privacy-preserving real-time traffic statistics. In: Proceedings of ACM GIS, pp. 540–543 (2017)Google Scholar
  11. 11.
    Ni, J., Zhang, K., Xia, Q., Lin, X., Shen, X.: Enabling strong privacy preservation and accurate task allocation for mobile crowdsensing. arXiv preprint arXiv:1806.04057 (2018)
  12. 12.
    Ni, J., Zhang, K., Yu, Y., Lin, X.: Providing task allocation and secure deduplication for mobile crowdsensing via fog computing. IEEE Trans. Depend. Secure Comput. PP(99), 1 (2018)CrossRefGoogle Scholar
  13. 13.
    Wang, L., Yang, D., Han, X.: Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation. In: Proceedings of ACM WWW, pp. 627–636 (2017)Google Scholar
  14. 14.
    Xiong, H., Zhang, D., Chen, G.: iCrowd: Near-optimal task allocation for piggyback crowdsensing. IEEE Trans. Mob. Comput. 15(8), 2010–2022 (2016)CrossRefGoogle Scholar
  15. 15.
    Wang, J., Wang, Y.: Multi-task allocation in mobile crowd sensing with individual task quality assurance. IEEE Trans. Mob. Comput. PP(99), 1 (2018)Google Scholar
  16. 16.
    Bordenabe, N., Chatzikokolakis, K.: Optimal geo-indistinguishable mechanisms for location privacy. In: Proceedings of ACM CCS, pp. 251–262 (2014)Google Scholar
  17. 17.
    Zhang, X., Gui, X.: Privacy quantification model based on the bayes conditional risk in location-based services. Tsinghua Sci. Technol. 19(5), 452–462 (2014)CrossRefGoogle Scholar
  18. 18.
    Shokri, R., Freudiger, J.: A distortion-based metric for location privacy. In: ACM Workshop on Privacy in the Electronic Society, pp. 21–30 (2009)Google Scholar
  19. 19.
    Shokri, R.: Privacy games: optimal user-centric data obfuscation. Proc. Priv. Enhanc. Technol. 2015(2), 299–315 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Mitchell, M.: Genetic algorithms: an overview. Complexity 1(1), 31–39 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing Tech UniversityNanjingChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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