Abstract
Spatial crowdsourcing system refers to sending various location-based tasks to workers according to their positions, and workers need to physically move to specified locations to accomplish tasks. The workers are restricted to report their real-time sensitive position to the server so as to keep in coordination with the crowdsourcing server. Therefore, implementing crowdsourcing system while preserving the privacy of workers sensitive information is a key issue that needs to be tackled. We discard the assumption of a trustworthy third party cellular service provider (CSP), and further propose a local method to achieve acceptable results. A differential privacy model ensures rigorous privacy guarantee, and Laplace mechanism noise is introduced to preserve workers sensitive information. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real-world datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
CrowdFlower. http://crowdower.com
Bhaskar, R., Laxman, S., Smith, A., Thakurta, A.: Discovering frequent patterns in sensitive data. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 503–512. ACM (2010)
Cao, C.C., She, J., Tong, Y., Chen, L.: Whom to ask?: jury selection for decision making tasks on micro-blog services. Proc. VLDB Endow. 5(11), 1495–1506 (2012)
Cao, C.C., Tong, Y., Chen, L., Jagadish, H.: WiseMarket: a new paradigm for managing wisdom of online social users. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 455–463. ACM (2013)
Chen, Z., Fu, R., Zhao, Z., Liu, Z., Xia, L., Chen, L., Cheng, P., Cao, C.C., Tong, Y., Zhang, C.J.: gMission: a general spatial crowdsourcing platform. Proc. VLDB Endow. 7(13), 1629–1632 (2014)
Cheng, P., Lian, X., Chen, L., Han, J., Zhao, J.: Task assignment on multi-skill oriented spatial crowdsourcing. IEEE Trans. Knowl. Data Eng. 28(8), 2201–2215 (2016)
Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., Yu, T.: Differentially private spatial decompositions. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 20–31. IEEE (2012)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Dwork, C.: Differential privacy in new settings. In: Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 174–183. SIAM (2010)
Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
Fan, L., Xiong, L., Sunderam, V.: Differentially private multi-dimensional time series release for traffic monitoring. In: Wang, L., Shafiq, B. (eds.) DBSec 2013. LNCS, vol. 7964, pp. 33–48. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39256-6_3
Gong, Y., Wei, L., Guo, Y., Zhang, C., Fang, Y.: Optimal task recommendation for mobile crowdsourcing with privacy control. IEEE Internet Things J. 3(5), 745–756 (2016)
Jia, O., Jian, Y., Shaopeng, L., Yubao, L.: An effective differential privacy transaction data publication strategy. J. Comput. Res. Dev. 10, 007 (2014)
Kazemi, L., Shahabi, C.: GeoCrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 189–198. ACM (2012)
Kazemi, L., Shahabi, C., Chen, L.: GeoTruCrowd: trustworthy query answering with spatial crowdsourcing. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 314–323. ACM (2013)
McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 94–103. IEEE (2007)
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 19–30. ACM (2009)
To, H., Ghinita, G., Shahabi, C.: A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endow. 7(10), 919–930 (2014)
Wang, J., Liu, S., Li, Y., Cao, H., Liu, M.: Differentially private spatial decompositions for geospatial point data. China Commun. 13(4), 97–107 (2016)
Xiao, Y., Gardner, J., Xiong, L.: DPCube: releasing differentially private data cubes for health information. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 1305–1308. IEEE (2012)
Xiao, Y., Xiong, L., Yuan, C.: Differentially private data release through multidimensional partitioning. In: Jonker, W., Petković, M. (eds.) SDM 2010. LNCS, vol. 6358, pp. 150–168. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15546-8_11
Xiong, P., Zhang, L., Zhu, T.: Reward-based spatial crowdsourcing with differential privacy preservation. Enterp. Inf. Syst. 11(10), 1500–1517 (2017)
Zhang, X., Wang, M., Meng, X.: An accurate method for mining top-k frequent pattern under differential privacy. J. Comput. Res. Dev. 51(1), 104–114 (2014)
Zhu, T., Li, G., Zhou, W., Philip, S.Y.: Differentially private data publishing and analysis: a survey. IEEE Trans. Knowl. Data Eng. 29(8), 1619–1638 (2017)
Acknowledgments
This work is partially supported by National Natural Science Foundation of China (NSFC) under Grant No. 61772491, No. 61472460, and Natural Science Foundation of Jiangsu Province under Grant No. BK20161256. Kai Han is the corresponding author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Xu, K., Han, K., Ye, H., Gao, F., Xu, C. (2018). Privacy-Preserving Personal Sensitive Data in Crowdsourcing. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-94268-1_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94267-4
Online ISBN: 978-3-319-94268-1
eBook Packages: Computer ScienceComputer Science (R0)