Abstract
With the increasing popularity of big data and sharing economics, spatial crowdsourcing as a new computing paradigm has attracted the attention of both academia and industry. Task allocation is one of the indispensable processes in spatial crowdsourcing, but how to allocate tasks efficiently while protecting location privacy of tasks and workers is a tough problem. Most of the existing works focus on the selection of the workers privately. Few of them present solutions for secure problems in task delivery. To address this problem, we propose a novel privacy protection scheme that not only protects the location privacy of workers and tasks but also enables secure delivery of tasks with very little overhead. We first use the paillier homomorphic cryptosystem to protect the privacy of workers and tasks, then calculate travel information securely. Finally, let workers restore the tasks’ location. In our scheme, only workers who meet the requirements can get the exact location of tasks. In addition, we prove the security of our method under the semi-honest model. Extensive experiments on real-world data sets demonstrate that our scheme achieves practical performance in terms of computational overhead and travel cost.
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References
Bugiel, S., Nurnberger, S., Sadeghi, A.R., Schneider, T.: Twin clouds: an architecture for secure cloud computing (2011)
Goldreich, O.: Foundations of cryptography. II: basic applications 2 (2004). https://doi.org/10.1017/CBO9780511721656
Goldwasser, S.: The knowledge complexity of interactive proof system. SIAM J. Comput. 18(1), 186–208 (1989)
Hai**, H., Tianhe, G., **, C., Reza, M., Tao, C.: Secure two-party distance computation protocol based on privacy homomorphism and scalar product in wireless sensor networks. Tsinghua Sci. Technol. (2016)
Howe, J.: The rise of crowdsourcing. Wired 14(6), 176–183 (2006)
Kazemi, L., Shahabi, C.: A privacy-aware framework for participatory sensing. ACM SIGKDD Explor. Newsl. 13(1), 43 (2011)
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. OpenProceedings.org (2017)
Liu, L., Chen, R., Liu, X., Su, J., Qiao, L.: Towards practical privacy-preserving decision tree training and evaluation in the cloud. IEEE Trans. Inf. Forensics Secur. 15, 2914–2929 (2020)
Miao, C., Jiang, W., Su, L., Li, Y., Guo, S.: Cloud-enabled privacy-preserving truth discovery in crowd sensing systems. In: ACM Conference on Embedded Networked Sensor Systems (2015)
Niu, B., Li, Q., Zhu, X., Cao, G., Hui, L.: Achieving K-anonymity in privacy-aware location-based services. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications (2014)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International Conference on Advances in Cryptology-Eurocrypt (1999)
Pournajaf, L., Li, X., Sunderam, V., Goryczka, S.: Spatial task assignment for crowd sensing with cloaked locations. In: IEEE International Conference on Mobile Data Management (2014)
Shen, Y., Huang, L., Li, L., Lu, X.: Towards preserving worker location privacy in spatial crowdsourcing. In: IEEE Global Communications Conference (2015)
To, H., Ghinita, G., Fan, L., Shahabi, C.: Differentially private location protection for worker datasets in spatial crowdsourcing. IEEE Trans. Mob. Comput. 16(4), 934–949 (2017)
To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endow. 7(10), 919–930 (2014)
To, H., Shahabi, C.: Location privacy in spatial crowdsourcing. In: Gkoulalas-Divanis, A., Bettini, C. (eds.) Handbook of Mobile Data Privacy, pp. 167–194. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98161-1_7
To, H., Shahabi, C., Kazemi, L.: A server-assigned spatial crowdsourcing framework. ACM Trans. Spat. Algorithms Syst. 1(1), 1–28 (2015)
Wang, Z., Hu, J., **g, Z., Yang, D., Chen, H., Qian, W.: Pay on-demand: dynamic incentive and task selection for location-dependent mobile crowdsensing systems. In: IEEE International Conference on Distributed Computing Systems (2018)
**ao, Y., **ong, L.: Protecting locations with differential privacy under temporal correlations, pp. 1298–1309 (2015). https://doi.org/10.1145/2810103.2813640
Yang, D., **, F., Xue, G.: Truthful incentive mechanisms for K-anonymity location privacy. In: Infocom. IEEE (2013)
Yuan, D., Li, Q., Lia, G., Wang, Q., Ren, K.: PriRadar: a privacy-preserving framework for spatial crowdsourcing. IEEE Trans. Inf. Forensics Secur., 1 (2019)
Zhai, D., et al.: Towards secure and truthful task assignment in spatial crowdsourcing. World Wide Web 22(5), 2017–2040 (2018). https://doi.org/10.1007/s11280-018-0638-2
Zhang, L., **ong, P., Ren, W., Zhu, T.: A differentially private method for crowdsourcing data submission. Concurrency Comput. Pract. Exp. 31, e5100 (2018)
Acknowledgment
This work is supported by the National Key Research and Development Program of China (No. 2018YFB0204301), National Nature Science Foundation of China (No. 62072466, No. U1811462), and the NUDT Grants (No. ZK19-38).
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Huang, X., Fu, S., Luo, Y., Lin, L. (2021). A Novel Location Privacy-Preserving Task Allocation Scheme for Spatial Crowdsourcing. In: **ong, J., Wu, S., Peng, C., Tian, Y. (eds) Mobile Multimedia Communications. MobiMedia 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-89814-4_23
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DOI: https://doi.org/10.1007/978-3-030-89814-4_23
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