Abstract
While spatial crowdsourcing has become a popular paradigm for spatio-temporal data collection, location privacy has raised increasing concerns among the participants of spatial crowdsourcing projects in recent years. The question of how to implement a spatial crowdsourcing project at minimal cost while preserving location privacy, is the major issue that most existing works have investigated. In this paper, we propose a novel privacy-preserving method for spatial crowdsourcing that combines location obfuscation and path optimization in order to provide enhanced privacy preservation at a minimal cost. We apply geo-indistinguishability and exponential mechanism to achieve an enhanced privacy guarantee. Moreover, because a higher privacy level consistently leads to extra distance cost, we therefore present a path optimization algorithm that reduces the total distance of a spatial crowdsourcing project. The experimental results demonstrate that the proposed method outperforms the traditional methods in terms of privacy level and performance costs.
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References
Agir B, Papaioannou TG, Narendula R, Aberer K, Hubaux JP (2014) User-side adaptive protection of location privacy in participatory sensing. Geoinformatica 18(1):165–191
Andrés ME, Bordenabe NE, Chatzikokolakis K, Palamidessi C (2013) Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC conference on computer; communications security, ACM, New York, NY, USA, CCS ’13, pp 901–914. https://doi.org/10.1145/2508859.2516735
Bentley JL (1990) K-d trees for semidynamic point sets. In: Proceedings of the sixth annual symposium on computational geometry, Association for Computing Machinery, New York, NY, USA, SCG ’90, pp 187–197. https://doi.org/10.1145/98524.98564
Chatzikokolakis K, Andrés ME, Bordenabe NE, Palamidessi C (2013) Broadening the scope of differential privacy using metrics. In: De Cristofaro E, Wright M (eds) Privacy enhancing technologies. Springer, Berlin, pp 82–102
Chatzikokolakis K, Palamidessi C, Stronati M (2015) Geo-indistinguishability: a principled approach to location privacy. In: Natarajan R, Barua G, Patra MR (eds) Distributed computing and internet technology. Springer International Publishing, Cham, pp 49–72
Chi Z, Wang Y, Huang Y, Tong X (2018) The novel location privacy-preserving CKD for mobile crowdsourcing systems. IEEE Access 6:5678–5687. https://doi.org/10.1109/ACCESS.2017.2783322
Cornelius C, Kapadia A, Kotz D, Peebles D, Shin M, Triandopoulos N (2008) Anonysense: privacy-aware people-centric sensing. In: Proceedings of the 6th international conference on mobile systems, applications, and services, ACM, New York, NY, USA, MobiSys ’08, pp 211–224. https://doi.org/10.1145/1378600.1378624
Dewri R (2013) Local differential perturbations: location privacy under approximate knowledge attackers. IEEE Trans Mob Comput 12(12):2360–2372. https://doi.org/10.1109/TMC.2012.208
Dwork C (2006) Differential privacy. In: ICALP’06: Proceedings of the 33rd international conference on automata, languages and programming, Springer-Verlag, Berlin, Heidelberg, pp 1–12. https://doi.org/10.1007/11787006_1
Hoh B, Gruteser M, Xiong H, Alrabady A (2006) Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Comput 5(4):38–46. https://doi.org/10.1109/MPRV.2006.69
Inan A, Kantarcioglu M, Ghinita G, Bertino E (2010) Private record matching using differential privacy. In: Proceedings of the 13th international conference on extending database technology, ACM, New York, NY, USA, EDBT ’10, pp 123–134. https://doi.org/10.1145/1739041.1739059
Kalnis P, Ghinita G, Mouratidis K, Papadias D (2008) Preventing location-based identity inference in anonymous spatial queries. IEEE Trans Knowl Data Eng 19:1719–1733. https://doi.org/10.1109/TKDE.2007.190662
Kazemi L, Shahabi C (2012) Geocrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th International conference on advances in geographic information systems, ACM, New York, NY, USA, pp 189–198. https://doi.org/10.1145/2424321.2424346
Kido H, Yanagisawa Y, Satoh T (2005) Protection of location privacy using dummies for location-based services. In: 21st International conference on data engineering workshops (ICDEW'05), pp 1248–1248. https://doi.org/10.1109/ICDE.2005.269
Liu B, Chen L, Zhu X, Zhang Y, Zhang C, Qiu W (2017) Protecting location privacy in spatial crowdsourcing using encrypted data. In: EDBT, pp 478–481
Matsuo Y, Okazaki N, Izumi K, Nakamura Y, Nishimura T, Hasida K, Nakashima H (2007) Inferring long-term user properties based on users’ location history. In: Proceedings of the 20th international joint conference on artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, IJCAI’07, pp 2159–2165. http://dl.acm.org/citation.cfm?id=1625275.1625624
McSherry F, Talwar K (2007) Mechanism design via differential privacy. In: Annual IEEE symposium on foundations of computer science (FOCS), IEEE. https://www.microsoft.com/en-us/research/publication/mechanism-design-via-differential-privacy/
Pournajaf L, Xiong L, Sunderam V, Xu X (2015) Stac: spatial task assignment for crowd sensing with cloaked participant locations. In: Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems, ACM, New York, NY, USA, SIGSPATIAL ’15, pp 90:1–90:4. https://doi.org/10.1145/2820783.2820788
Pournajaf L, Garcia-Ulloa DA, Xiong L, Sunderam V (2016) Participant privacy in mobile crowd sensing task management: a survey of methods and challenges. ACM Sigmod Rec 44(4):23–34. https://doi.org/10.1145/2935694.2935700
Shen Y, Huang L, Li L, Lu X, Wang S, Yang W (2015) Towards preserving worker location privacy in spatial crowdsourcing. In: 2015 IEEE global communications conference (GLOBECOM), pp 1–6. https://doi.org/10.1109/GLOCOM.2015.7416965
Shen H, Bai G, Hu Y, Wang T (2019) P2TA: privacy-preserving task allocation for edge computing enhanced mobile crowdsensing. J Syst Archit 97:130–141. https://doi.org/10.1016/j.sysarc.2019.01.005
Squicciarini AC, Qiu C (2019) Location privacy protection in vehicle-based spatial crowdsourcing via geo-indistinguishability. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), pp 1061–1071. https://doi.org/10.1109/ICDCS.2019.00109
To H, Ghinita G, Shahabi C (2014) A framework for protecting worker location privacy in spatial crowdsourcing. Proc VLDB Endow 7(10):919–930. https://doi.org/10.14778/2732951.2732966
To H, Shahabi C, Xiong L (2018) Privacy-preserving online task assignment in spatial crowdsourcing with untrusted server. In: 2018 IEEE 34th international conference on data engineering (ICDE), pp 833–844. https://doi.org/10.1109/ICDE.2018.00080
Vergara-Laurens IJ, Mendez D, Labrador MA (2014) Privacy, quality of information, and energy consumption in participatory sensing systems. In: 2014 IEEE international conference on pervasive computing and communications (PerCom), pp 199–207. https://doi.org/10.1109/PerCom.2014.6813961
Wang S, Wang X (2010) In-device spatial cloaking for mobile user privacy assisted by the cloud. In: 2010 Eleventh international conference on mobile data management, pp 381–386. https://doi.org/10.1109/MDM.2010.82
Wang L, Yang D, Han X, Tianben W, Zhang D, Ma X (2017) Location privacy-preserving task allocation for mobile crowdsensing with diferential geo-obfuscation. In: Proceedings of the 26th International conference on world wide web (WWW '17), pp 627–636. https://doi.org/10.1145/3038912.3052696
Wang Z, Hu J, Lv R, Wei J, Wang Q, Yang D, Qi H (2018) Personalized privacy-preserving task allocation for mobile crowdsensing. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2018.2861393
Xiong P, Zhang L, Zhu T (2017) Reward-based spatial crowdsourcing with differential privacy preservation. Enterp Inf Syst 11(10):1500–1517. https://doi.org/10.1080/17517575.2016.1253874
Xiong P, Zhu D, Zhang L, Ren W, Zhu T (2019) Optimizing rewards allocation for privacy-preserving spatial crowdsourcing. Comput Commun. https://doi.org/10.1016/j.comcom.2019.07.020
Yan K, Lu G, Luo G, Zheng X, Tian L, Sai A (2019) Location privacy-aware task bidding and assignment for mobile crowd-sensing. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2940738
Yuan D, Li Q, Lia G, Wang Q, Ren K (2019) PriRadar: a privacy-preserving framework for spatial crowdsourcing. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2019.2913232
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This work is supported by the National Natural Science Foundation of China under Grant No. 61972366, and the Humanities and Social Sciences Planning Project of the China Ministry of Education under Grant No. 19YJAZH099.
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Xiong, P., Li, G., Ren, W. et al. LOPO: a location privacy preserving path optimization scheme for spatial crowdsourcing. J Ambient Intell Human Comput 13, 5803–5818 (2022). https://doi.org/10.1007/s12652-021-03266-x
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DOI: https://doi.org/10.1007/s12652-021-03266-x