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A Differentially Private Method for Reward-Based Spatial Crowdsourcing

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Applications and Techniques in Information Security (ATIS 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 557))

Abstract

The popularity of mobile devices such as smart phones and tablets has led to a growing use of spatial crowdsourcing in recent years. However, current solution requires the workers send their locations to a centralized server, which leads to a privacy threat. One of the key challenges of spatial crowdsourcing is to maximize the number of assigned tasks with workers’ location privacy preserved. In this paper, we focus on the reward-based spatial crowdsourcing and propose a two-stage method which consists of constructing a differentially private contour plot followed by task assignment with optimized-reward allocation. Experiments on real dataset demonstrate the availability of the proposed method.

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References

  1. Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer; Communications Security, CCS 2013, pp. 901–914. ACM, New York (2013)

    Google Scholar 

  2. Dewri, R.: Local differential perturbations: location privacy under approximate knowledge attackers. IEEE Trans. Mob. Comput. 12(12), 2360–2372 (2013)

    Article  Google Scholar 

  3. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)

    Article  Google Scholar 

  6. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Kazemi, L., Shahabi, C.: Geocrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2012, pp. 189–198. ACM, New York (2012)

    Google Scholar 

  8. Li, N., Yang, W., Qardaji, W.: Differentially private grids for geospatial data. In: Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), pp. 757–768. IEEE Computer Society, Washington, D.C. (2013)

    Google Scholar 

  9. Lu, H., Jensen, C.S., Yiu, M.L.: Pad: privacy-area aware, dummy-based location privacy in mobile services. In: Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE 2008, pp. 16–23. ACM, New York (2008)

    Google Scholar 

  10. McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 94–103. IEEE Computer Society, Washington, D.C. (2007)

    Google Scholar 

  11. Pan, X., Xu, J., Meng, X.: Protecting location privacy against location-dependent attack in mobile services. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 1475–1476. ACM, New York (2008)

    Google Scholar 

  12. Pournajaf, L., Xiong, L., Sunderam, V., Goryczka, S.: Spatial task assignment for crowd sensing with cloaked locations. In: Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management, MDM 2014, vol. 01, pp. 73–82. IEEE Computer Society, Washington, D.C. (2014)

    Google Scholar 

  13. 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 

  14. To, H., Ghinita, G., Shahabi, C.: Privgeocrowd: a toolbox for studying private spatial crowdsourcing. In: Proceedings of the 31st IEEE International Conference on Data Engineering (2015)

    Google Scholar 

  15. Xiao, Y., Gardner, J., Xiong, L.: Dpcube: releasing differentially private data cubes for health information. In: Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, ICDE 2012, pp. 1305–1308. IEEE Computer Society, Washington, D.C. (2012)

    Google Scholar 

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Acknowledgements

This work is supported by the Natural Science Foundation of HuBei province (China) under Grant No. 2014CFB354 and the Fundamental Research Funds for Central Universities of China under Grant No. 31541511301.

Author information

Authors and Affiliations

  1. School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Lefeng Zhang, Xiaodan Lu & Ping Xiong

  2. School of Information Technology, Deakin University, Geelong, Australia

    Tianqing Zhu

Authors
  1. Lefeng Zhang
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  2. Xiaodan Lu
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  3. Ping Xiong
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  4. Tianqing Zhu
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Corresponding author

Correspondence to Lefeng Zhang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Wenjia Niu

  2. Deakin University, Burwood, Victoria, Australia

    Gang Li

  3. Beijing Jiaotong University, Beijing, China

    Jiqiang Liu

  4. Chinese Academy of Sciences, Beijing, China

    Jianlong Tan

  5. Chinese Academy of Sciences, Beijing, China

    Li Guo

  6. Beijing Jiaotong University, Beijing, China

    Zhen Han

  7. Fac. Science&Technology, Deakin University, Burwood, Victoria, Australia

    Lynn Batten

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Zhang, L., Lu, X., Xiong, P., Zhu, T. (2015). A Differentially Private Method for Reward-Based Spatial Crowdsourcing. In: Niu, W., et al. Applications and Techniques in Information Security. ATIS 2015. Communications in Computer and Information Science, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48683-2_14

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  • DOI: https://doi.org/10.1007/978-3-662-48683-2_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48682-5

  • Online ISBN: 978-3-662-48683-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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