Skip to main content
SpringerLink
Log in
Book cover

International Conference on Wireless Algorithms, Systems, and Applications

WASA 2017: Wireless Algorithms, Systems, and Applications pp 755–767Cite as

A User Incentive-Based Scheme Against Dishonest Reporting in Privacy-Preserving Mobile Crowdsensing Systems

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10251))

Abstract

Proliferating Mobile Crowdsensing Systems (MCSs) is a promising paradigm to realize large-scale sensing targets in an agile and economical manner. Privacy protection mechanisms, which alleviate mobile user’s concern on participating MCS tasks, also introduce the issue of data quality to the MCS server. In privacy-preserving MCSs, dishonest reporting of mobile sensing data from task participants could severely affect the MCS sensing accuracy. In this paper, we develop a user incentive-based scheme against dishonest reporting in privacy-preserving MCSs. Our proposed scheme is capable of improving the MCS sensing accuracy by encouraging users to honestly upload obtained sensing information for a higher serving profit. The performance of our scheme is evaluated via extensive real-world trace-driven simulations. Our experimental results show that our scheme can effectively ensure MCS sensing accuracy while encouraging honest reporting.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    As this can be achieved according to anonymous communications described in [19,20,21,22], we provide no further discussion.

References

  1. Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N., Huang, R., Zhou, X.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM CSUR 48(1), 7 (2015)

    Google Scholar 

  2. Duan, Z., Li, W., Cai, Z.: Distributed auctions for task assignment and scheduling in mobile crowdsensing systems. In: Proceedings of IEEE ICDCS (2017)

    Google Scholar 

  3. Capezzuto, L., Abbamonte, L., De Vito, S., Massera, E.: A maker friendly mobile and social sensing approach to urban air quality monitoring. In: Proceedings of IEEE Sensors, pp. 12–16 (2014)

    Google Scholar 

  4. Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., Bian, K., Wang, T., Li, X.: Jigsaw: indoor floor plan reconstruction via mobile crowdsensing. In: Proceedings of ACM Mobicom, pp. 249–260 (2014)

    Google Scholar 

  5. Bakht, M., Trower, M., Kravets, R.: Searchlight: won’t you be my neighbor? In: Proceedings of ACM Mobicom, pp. 185–196 (2012)

    Google Scholar 

  6. Li, J., Cai, Z., Yan, M., Li, Y.: Using crowdsourced data in location-based social networks to explore influence maximization. In: Proceedings of IEEE Infocom (2016)

    Google Scholar 

  7. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet-of-Things (IoT) J. (2017)

    Google Scholar 

  8. Ren, X., Yang, X., Lin, J., Yu, W.: On binary decomposition based privacy-preserving aggregation schemes in real-time monitoring systems. IEEE Trans. Parallel Distrib. Syst. 27(10), 2967–2983 (2016)

    Article  Google Scholar 

  9. Yurur, O., Liu, C., Sheng, Z., Leung, V.: Context-awareness for mobile sensing: a survey and future directions. IEEE Commun. Surv. Tuts. 18(1), 68–93 (2016)

    Article  Google Scholar 

  10. Duan, Z., Yan, M., Cai, Z., Wang, X., Han, M., Li, Y.: Truthful incentive mechanisms for social cost minimization in mobile crowdsourcing systems. Sensors 16(4), 481 (2016)

    Article  Google Scholar 

  11. Zhao, C., Yang, S., Yang, X., McCann, J.: Rapid, user-transparent, and trustworthy device pairing for D2D-enabled mobile crowdsourcing. IEEE Trans. Mob. Comput. (99), 1 (2016)

    Google Scholar 

  12. Na, R., Gao, L., Zhu, H., Jia, W., Li, X., Hu, Q.: Toward optimal dos-resistant authentication in crowdsensing networks via evolutionary game. In: Proceedings of IEEE ICDCS, pp. 364–373 (2016)

    Google Scholar 

  13. He, D., Chan, S., Guizani, M.: User privacy and data trustworthiness in mobile crowd sensing. IEEE Wirel. Commun. 22(1), 28–34 (2015)

    Article  Google Scholar 

  14. Wang, Y., Cai, Z., Ying, G., Gao, Y., Tong, X., Wu, G.: An incentive mechanism with privacy protection in mobile crowdsourcing systems. Comput. Netw. 102, 157–171 (2016)

    Article  Google Scholar 

  15. Wang, X., Cheng, W., Mohapatra, P., Abdelzaher, T.: Enabling reputation and trust in privacy-preserving mobile sensing. IEEE Trans. Mob. Comput. 13(12), 2777–2790 (2014)

    Article  Google Scholar 

  16. Wang, W., Zhang, Q.: Location privacy preservation in collaborative spectrum sensing. In: Proceedings of IEEE Infocom, pp. 729–737 (2012)

    Google Scholar 

  17. To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endow. 7(10), 919–930 (2014)

    Article  Google Scholar 

  18. Li, Q., Cao, G.: Providing efficient privacy-aware incentives for mobile sensing. In: Proceedings of IEEE ICDCS, pp. 208–217 (2014)

    Google Scholar 

  19. Ling, Z., Yang, M., Lou, J., Fu, X., Yu, W.: De-anonymizing and countermeasures in anonymous communication networks. IEEE Commun. Mag. 53(4), 60–66 (2015)

    Article  Google Scholar 

  20. Pingley, A., Yu, W., Zhang, N., Fu, X., Zhao, W.: Cap: a context-aware privacy protection system for location-based services. In: Proceedings of IEEE ICDCS, pp. 49–57 (2009)

    Google Scholar 

  21. Yu, W., Fu, X., Graham, S., Xuan, D., Zhao, W.: DSSS-based flow marking technique for invisible traceback. In: Proceedings of IEEE S&P, pp. 18–32 (2007)

    Google Scholar 

  22. Ling, Z., Luo, J., Yu, W., Fu, X., Xuan, D., Jia, W.: A new cell-counting-based attack against Tor. IEEE/ACM Trans. Netw. 20(4), 1245–1261 (2012)

    Article  Google Scholar 

  23. Zhao, C., Yang, X., Yu, W., Yao, X., Lin, J., Li, X.: Cheating-resilient incentive scheme for mobile crowdsensing systems. In: Proceedings of IEEE CCNC, pp. 1–6 (2017)

    Google Scholar 

  24. Yang, S., Adeel, U., McCann, J.: Backpressure meets taxes: faithful data collection in stochastic mobile phone sensing systems. In: Proceedings of IEEE Infocom, pp. 1490–1498 (2015)

    Google Scholar 

  25. Alswailim, M.A,. Hassanein, H.S., Zulkernine, M.: CRAWDAD dataset queensu/crowd_temperature (v.2015-11-20) (2015). http://crawdad.org/queensu/crowd_temperature/20151120

  26. Li, X., Zhou, F., Yang, X.: Scalable feedback aggregating (SFA) overlay for large-scale P2P trust management. IEEE Trans. Parallel Distrib. Syst. 23(10), 1944–1957 (2012)

    Article  Google Scholar 

  27. Shen, H., Lin, Y., Sapra, K., Li, Z.: Enhancing collusion resilience in reputation systems. IEEE Trans. Parallel Distrib. Syst. 27(8), 2274–2287 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Xinyu Yang & Cong Zhao

  2. Department of Computer and Information Sciences, Towson University, Towson, USA

    Wei Yu

  3. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Xianghua Yao

  4. Department of Computer Science, University of Massachusetts Lowell, Lowell, USA

    Xinwen Fu

Authors
  1. Xinyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xianghua Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinwen Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong Zhao .

Editor information

Editors and Affiliations

  1. Texas Christian University, Fort Worth, Texas, USA

    Liran Ma

  2. New Jersey Institute of Technology, Newark, New Jersey, USA

    Abdallah Khreishah

  3. University of Oslo, Oslo, Norway

    Yan Zhang

  4. University of North Georgia, Dahlonega, Georgia, USA

    Mingyuan Yan

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Yang, X., Zhao, C., Yu, W., Yao, X., Fu, X. (2017). A User Incentive-Based Scheme Against Dishonest Reporting in Privacy-Preserving Mobile Crowdsensing Systems. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60033-8_64

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60032-1

  • Online ISBN: 978-3-319-60033-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation