Privacy-Preservation Using Group Signature for Incentive Mechanisms in Mobile Crowd Sensing

  • Kim, Mihui (Dept. of Computer Science & Engineering, Computer System Institute, Hankyong National University) ;
  • Park, Younghee (Dept. of Computer Engineering Department, San Jose State University) ;
  • Dighe, Pankaj Balasaheb (Dept. of Computer Engineering Department, San Jose State University)
  • Received : 2018.08.31
  • Accepted : 2019.04.18
  • Published : 2019.10.31


Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.


Incentive Method;Internet of Things (IoT) Model;Mobile Crowd Sensing (MCS);Privacy-Preserving;Using Group Signature


Supported by : National Research Foundation of Korea (NRF)


  1. D. Evans, "The Internet of Things: how the next evolution of the internet is changing everything," Cisco Internet Business Solutions Group (IBSG), San Jose, CA, 2011.
  2. M. Kanellos, "What's The Big Data?," 2016;
  3. A. Botta, W. De Donato, V. Persico, and A. Pescape, "On the integration of cloud computing and Internet of Things," in Proceedings of 2014 International Conference on Future Internet of Things and Cloud, Barcelona, Spain, 2014, pp. 23-30.
  4. X. Sheng, J. Tang, X. Xiao, and G. Xue, "Sensing as a service: challenges, solutions and future directions," IEEE Sensors Journal, vol. 13, no. 10, pp. 3733-3741, 2013.
  5. R. K. Ganti, F. Ye, and H. Lei, "Mobile crowdsensing: current state and future challenges," IEEE Communications Magazine, vol. 49, no. 11, pp. 32-39, 2011.
  6. X. Jin and Y. Zhang, "Privacy-preserving crowdsourced spectrum sensing," IEEE/ACM Transactions on Networking (TON), vol. 26, no. 3, pp. 1236-1249, 2018.
  7. E. Macias, A. Suarez, and J. Lloret, "Mobile sensing systems," Sensors, vol. 13, no. 12, pp. 17292-17321, 2013.
  8. L. G. Jaimes, I. Vergara-Laurens, and M. A. Labrador, "A location-based incentive mechanism for participatory sensing systems with budget constraints," in Proceedings of 2012 IEEE International Conference on Pervasive Computing and Communications, Lugano, Switzerland, 2012, pp. 103-108.
  9. Y. Wen, J. Shi, Q. Zhang, X. Tian, Z. Huang, H. Yu, Y. Cheng, and X. Shen, "Quality-driven auction-based incentive mechanism for mobile crowd sensing," IEEE Transactions on Vehicular Technology, vol. 64, no. 9, pp. 4203-4214, 2015.
  10. V. S. Pulla, C. S. Jammi, P. Tiwari, M. Gjoka, and A. Markopoulou, "QuestCrowd: a location-based question answering system with participation incentives," in Proceedings of 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Turin, Italy, 2013, pp. 75-76.
  11. Q. Xie and L. Wang, "Privacy-preserving location-based service scheme for mobile sensing data," Sensors, vol. 16, article no. 1993, 2016.
  12. G. Danezis, S. Lewis, and R. J. Anderson, "How much is location privacy worth?," in Proceedings of the 4th Annual Workshop on the Economics of Information Security (WEIS), Cambridge, MA, 2005.
  13. A. Singla and A. Krause, "Incentives for privacy tradeoff in community sensing," in Proceedings of the 1st AAAI Conference on Human Computation and Crowdsourcing, Palm Spring, CA, 2013.
  14. H. Jin, L. Su, B. Ding, K. Nahrstedt, and N. Borisov, "Enabling privacy-preserving incentives for mobile crowd sensing systems," in Proceedings of 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan, 2016, pp. 344-353.
  15. J. Sun and H. Ma, "Privacy-preserving verifiable incentive mechanism for online crowdsourcing markets," in Proceedings of 2014 23rd International Conference on Computer Communication and Networks (ICCCN), Shanghai, China, 2014, pp. 1-8.
  16. S. Gisdakis, T. Giannetsos, and P. Papadimitratos, "Security, privacy, and incentive provision for mobile crowd sensing systems," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 839-853, 2016.
  17. X. Li, M. Miao, H. Liu, J. Ma, and K. C. Li, "An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism," Soft Computing, vol. 21, no. 14, pp. 3907-3917, 2017.
  18. X. Niu, M. Li, Q. Chen, Q. Cao, and H. Wang, "EPPI: an e-cent-based privacy-preserving incentive mechanism for participatory sensing systems," in Proceedings of 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC), Austin, TX, 2014, pp. 1-8.
  19. Y. Gong, Y. Cai, Y. Guo, and Y. Fang, "A privacy-preserving scheme for incentive-based demand response in the smart grid," IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1304-1313, 2016.
  20. K. Potzmader, J. Winter, D. Hein, C. Hanser, P. Teufl, and L. Chen, "Group signatures on mobile devices: practical experiences," in Trust and Trustworthy Computing. Heidelberg: Springer, 2013, pp. 47-64.
  21. M. Kim, "Incentive mechanism with privacy-preservation on intelligent parking system utilizing mobile crowdsourcing," in Proceedings of 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali, Indonesia, 2017, pp. 1-4.
  22. R. Huang, B. Ying, and A. Nayak, "Protecting location privacy in opportunistic mobile social networks," in Proceedings of 2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, 2018, pp. 1-8.
  23. P. Zhao, J. Li, F. Zeng, F. Xiao, C. Wang, and H. Jiang, "ILLIA: enabling k-anonymity-based privacy preserving against location injection attacks in continuous LBS queries," IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1033-1042, 2018.
  24. B. Ying and A. Nayak, "Social location privacy protection method in vehicular social networks," in Proceedings of 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France, 2017, pp. 1288-1292.
  25. D. Chaum and E. Van Heyst, "Group signatures," in Advances in Cryptology-EUROSCRIPT'91. Heidelberg: Springer, 1991, pp. 257-265.
  26. M. E. Andres, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, "Geo-indistinguishability: differential privacy for location-based systems," in Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security (CCS'13), Berlin, Germany, 2013, pp. 901-914.
  27. V. Primault, S. B. Mokhtar, C. Lauradoux, and L. Brunie, "Differentially private location privacy in practice," in Proceedings of the 3rd Workshop on Mobile Security Technologies (MoST), San Jose, CA, 2014.
  28. L. Chen, D. Page, and N. P. Smart, "On the design and implementation of an efficient DAA scheme," in Smart Card Research and Advanced Applications. Heidelberg: Springer, 2010, pp. 223-237.
  29. ISO20008-2.2 Group Signature Scheme Evaluation on Mobile Devices,
  30. Amazon EC2 Instance Types,
  31. Intel IOT Developer Kit,