Location Privacy in Spatial Crowdsourcing
- 724 Downloads
Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks’ locations. However, current solutions require the locations of the workers and/or the tasks to be disclosed to untrusted entities (SC server) for effective assignments of tasks to workers.
This chapter first identifies privacy threats toward both workers and tasks during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC server). The latter phase is reporting, in which workers travel to the tasks’ locations, complete the tasks and upload their reports to the server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks).
This chapter aims to provide an overview of the state-of-the-art in protecting users’ location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work.
- 1.iRain: new mobile app to promote citizen-science and support water management: http://en.unesco.org/news/irain-new-mobile-app-promote-citizen-science-and-support-water-management, 2016.
- 2.I. Boutsis and V. Kalogeraki. Privacy preservation for participatory sensing data. In 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 103–113. IEEE, mar 2013.Google Scholar
- 5.D. Deng, C. Shahabi, and U. Demiryurek. Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing. In Proc. 21st ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst. - SIGSPATIAL’13, pages 314–323, New York, New York, USA, 2013. ACM Press.Google Scholar
- 6.C. Dwork. Differential privacy. In Automata, languages and programming, pages 1–12. Springer, 2006.Google Scholar
- 7.Ú. Erlingsson, V. Pihur, and A. Korolova. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In SIGSAC, pages 1054–1067. ACM, 2014.Google Scholar
- 8.Y. Gong, C. Zhang, Y. Fang, and J. Sun. Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing. In IEEE Transactions on Emerging Topics in Computing, pages 1–1, 2015.Google Scholar
- 9.A. Greenberg. Apple’s “differential privacy’ is about collecting your data - but not your data. https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/, 2016.
- 10.J. Hu, L. Huang, L. Li, M. Qi, and W. Yang. Protecting Location Privacy in Spatial Crowdsourcing. In Asia-Pacific Web Conference, pages 113–124. Springer International Publishing, 2015.Google Scholar
- 11.L. Hu and C. Shahabi. Privacy assurance in mobile sensing networks: Go beyond trusted servers. In 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2010, pages 613–619, 2010.Google Scholar
- 12.L. Kazemi and C. Shahabi. A privacy-aware framework for participatory sensing. In ACM SIGKDD Explorations Newsletter, volume 13, page 43. ACM, aug 2011.Google Scholar
- 13.L. Kazemi and C. Shahabi. GeoCrowd: Enabling Query Answering with Spatial Crowdsourcing. In Proc. 20th Int. Conf. Adv. Geogr. Inf. Syst. - SIGSPATIAL ’12, number c, page 189, 2012.Google Scholar
- 14.L. Kazemi, C. Shahabi, and L. Chen. GeoTruCrowd: Trustworthy Query Answering with Spatial Crowdsourcing. In Proc. 21st ACM SIGSPATIAL Int. Conf. Adv. Geogr. Inf. Syst. - SIGSPATIAL’13, pages 304–313, 2013.Google Scholar
- 15.S. H. Kim, Y. Lu, G. Constantinou, C. Shahabi, G. Wang, and R. Zimmermann. Mediaq: mobile multimedia management system. In Proceedings of the 5th ACM Multimedia Systems Conference, pages 224–235. ACM, 2014.Google Scholar
- 17.B. Liu, L. Chen, X. Zhu, Y. Zhang, C. Zhang, and W. Qiu. Protecting location privacy in spatial crowdsourcing using encrypted data. In EDBT, pages 478–481, 2017.Google Scholar
- 19.J. C. Navas and T. Imielinski. Geocast: geographic addressing and routing. In Proceedings of the 3rd annual ACM/IEEE international conference on Mobile computing and networking, pages 66–76. ACM, 1997.Google Scholar
- 21.L. Pournajaf, D. A. Garcia-ulloa, L. Xiong, and V. Sunderam. Participant Privacy in Mobile Crowd Sensing Task Management : A Survey of Methods and Challenges. In SIGMOD Record, volume 44, pages 23–34. ACM, may 2015.Google Scholar
- 22.L. Pournajaf, L. Xiong, V. Sunderam, and S. Goryczka. Spatial task assignment for crowd sensing with cloaked locations. In Proceedings - IEEE International Conference on Mobile Data Management, volume 1, pages 73–82. IEEE, jul 2014.Google Scholar
- 23.W. Qardaji, W. Yang, and N. Li. Differentially private grids for geospatial data. In 2013 IEEE 29th International Conference on Data Engineering (ICDE), pages 757–768. IEEE, 2013.Google Scholar
- 24.D. Reinhardt and F. Dürr. Opportunities and risks of delegating sensing tasks to the crowd. In Handbook on Mobile Data Privacy. Springer, 2017.Google Scholar
- 25.J. Scheck. Stalkers exploit cellphone GPS. http://www.wsj.com, 2010.
- 26.Y. Shen, L. Huang, L. Li, X. Lu, S. Wang, and W. Yang. Towards preserving worker location privacy in spatial crowdsourcing. In 2015 IEEE Global Communications Conference, GLOBECOM 2015, 2016.Google Scholar
- 28.Y. Sun, A. Liu, Z. Li, G. Liu, L. Zhao, and K. Zheng. Anonymity-based privacy-preserving task assignment in spatial crowdsourcing. In International Conference on Web Information Systems Engineering, pages 263–277. Springer, 2017.Google Scholar
- 30.H. To, L. Fan, L. Tran, and C. Shahabi. Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints. In 2016 IEEE Int. Conf. Pervasive Comput. Commun. PerCom 2016, pages 1–8. IEEE, mar 2016.Google Scholar
- 31.H. To, G. Ghinita, L. Fan, and C. Shahabi. Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing. In IEEE Transactions on Mobile Computing, volume PP, pages 1–1, 2016.Google Scholar
- 32.H. To, G. Ghinita, and C. Shahabi. A framework for protecting worker location privacy in spatial crowdsourcing. In Proceedings of the VLDB Endowment, volume 7, pages 919–930. VLDB Endowment, jun 2014.Google Scholar
- 33.H. To, G. Ghinita, and C. Shahabi. PrivGeoCrowd: A toolbox for studying private spatial Crowdsourcing. In Proceedings - International Conference on Data Engineering, volume 2015-May, pages 1404–1407. IEEE, apr 2015.Google Scholar
- 35.K. Vu, R. Zheng, and J. Gao. Efficient algorithms for K-anonymous location privacy in participatory sensing. In Proceedings - IEEE INFOCOM, pages 2399–2407, 2012.Google Scholar
- 36.G. Wang, B. Wang, T. Wang, A. Nika, H. Zheng, and B. Y. Zhao. Defending against Sybil Devices in Crowdsourced Mapping Services. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys ’16, pages 179–191, 2016.Google Scholar