Towards Privacy-Preserving Travel-Time-First Task Assignment in Spatial Crowdsourcing

  • Jian Li
  • An LiuEmail author
  • Weiqi Wang
  • Zhixu Li
  • Guanfeng Liu
  • Lei Zhao
  • Kai Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


With the ubiquity of mobile devices and wireless networks, spatial crowdsourcing (SC) has gained considerable popularity and importance as a new tool of problem-solving. It enables complex tasks at specific locations to be performed by a crowd of nearby workers. In this paper, we study the privacy-preserving travel-time-first task assignment problem where tasks are assigned to workers who can arrive at the required locations first and no private information are revealed to unauthorized parties. Compared with existing work on privacy-preserving task assignment, this problem is novel as tasks are allocated according to travel time rather than travel distance. Moreover, it is challenging as secure computation of travel time requires secure division which is still an open problem nowadays. Observing that current solutions for secure division do not scale well, we propose an efficient algorithm to securely calculate the least common multiple (LCM) of every workers speed, based on which expensive division operation on ciphertexts can be avoided. We formally prove that our protocol is secure against semi-honest adversaries. Through extensive experiments over real datasets, we demonstrate the efficiency and effectiveness of our proposed protocol.


Spatial crowdsourcing Privacy-preserving Task assignment 



Research reported in this publication was partially supported Natural Science Foundation of China (Grant Nos. 61572336, 61632016, 61572335).


  1. 1.
    Chen, L., Shahabi, C.: Spatial crowdsourcing: challenges and opportunities. IEEE Data Eng. Bull. 39(4), 14–25 (2016)Google Scholar
  2. 2.
    Kazemi, L., Shahabi, C.: GeoCrowd: enabling query answering with spatial crowdsourcing. In: SIGSPATIAL, pp. 189–198 (2012)Google Scholar
  3. 3.
    Deng, D., Shahabi, C., Demiryurek, U.: Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In: SIGSPATIAL, pp. 324–333 (2013)Google Scholar
  4. 4.
    Cheng, P., Lian, X., Chen, Z., Fu, R., Chen, L., Han, J., Zhao, J.: Reliable diversity-based spatial crowdsourcing by moving workers. PVLDB 8(10), 1022–1033 (2015)Google Scholar
  5. 5.
    To, H., Ghinita, G., Fan, L., Shahabi, C.: Differentially private location protection for worker datasets in spatial crowdsourcing. TMC 16(4), 934–949 (2017)Google Scholar
  6. 6.
    Liu, B., Chen, L., Zhu, X., Zhang, Y., Zhang, C., Qiu, W.: Protecting location privacy in spatial crowdsourcing using encrypted data. In: EDBT, pp. 478–481 (2017)Google Scholar
  7. 7.
    To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. PVLDB 7(10), 919–930 (2014)Google Scholar
  8. 8.
    Liu, A., Li, Z., Liu, G., Zheng, K., Zhang, M., Li, Q., Zhang, X.: Privacy-preserving task assignment in spatial crowdsourcing. J. Comput. Sci. Technol. 32(5), 905–918 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, A., Wang, W., Shang, S., Li, Q., Zhang, X.: Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica 22(2), 335–362 (2018)CrossRefGoogle Scholar
  10. 10.
    Cheng, P., Lian, X., Chen, L., Han, J., Zhao, J.: Task assignment on multi-skill oriented spatial crowdsourcing. TKDE 28(8), 2201–2215 (2016)Google Scholar
  11. 11.
    Zheng, L., Chen, L.: Maximizing acceptance in rejection-aware spatial crowdsourcing. TKDE 29(9), 1943–1956 (2017)Google Scholar
  12. 12.
    Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: ICDE, pp. 49–60 (2016)Google Scholar
  13. 13.
    Tong, Y., Wang, L., Zhou, Z., Ding, B., Chen, L., Ye, J., Xu, K.: Flexible online task assignment in real-time spatial data. PVLDB 10(11), 1334–1345 (2017)Google Scholar
  14. 14.
    Gao, D., Tong, Y., She, J., Song, T., Chen, L., Xu, K.: Top-k team recommendation and its variants in spatial crowdsourcing. Data Sci. Eng. 2(2), 136–150 (2017)CrossRefGoogle Scholar
  15. 15.
    Paulet, R., Kaosar, M.G., Yi, X., Bertino, E.: Privacy-preserving and content-protecting location based queries. TKDE 26(5), 1200–1210 (2014)Google Scholar
  16. 16.
    Liu, S., et al.: Efficient query processing with mutual privacy protection for location-based services. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9643, pp. 299–313. Springer, Cham (2016). Scholar
  17. 17.
    Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical k nearest neighbor queries with location privacy. In: ICDE, pp. 640–651 (2014)Google Scholar
  18. 18.
    Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical approximate k nearest neighbor queries with location and query privacy. TKDE 28(6), 1546–1559 (2016)Google Scholar
  19. 19.
    Sun, Y., Liu, A., Li, Z., Liu, G., Zhao, L., Zheng, K.: Anonymity-based privacy-preserving task assignment in spatial crowdsourcing. In: Bouguettaya, A., et al. (eds.) WISE 2017. LNCS, vol. 10570, pp. 263–277. Springer, Cham (2017). Scholar
  20. 20.
    Liu, A., Zheng, K., Li, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: ICDE, pp. 66–77 (2015)Google Scholar
  21. 21.
    Goldreich, O.: The Foundations of Cryptography - Volume 2, Basic Applications. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  22. 22.
    Reddaway, S.: Pseudo-random number generators. US, pp. 57–67 (1974)Google Scholar
  23. 23.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). Scholar
  24. 24.
    ElGamal, T.: A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Trans. Inf. Theory 31(4), 469–472 (1985)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Li, Q., Cao, G., La Porta, T.F.: Efficient and privacy-aware data aggregation in mobile sensing. TDSC 11(2), 115–129 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jian Li
    • 1
  • An Liu
    • 1
    Email author
  • Weiqi Wang
    • 1
  • Zhixu Li
    • 1
  • Guanfeng Liu
    • 1
  • Lei Zhao
    • 1
  • Kai Zheng
    • 2
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.University of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations