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Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation

Published: 03 April 2017 Publication History

Abstract

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.

References

[1]
MOSEK. https://www.mosek.com/, 2016. Accessed: 2016--10--17.
[2]
WAZE - Google Play. https://play.google.com/store/apps/details?id=com.waze&hl=en, 2016. Accessed: 2016--10--17.
[3]
M. E. Andrés, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi. Geo-indistinguishability: Differential privacy for location-based systems. In CCS, pages 901--914, 2013.
[4]
P. Belotti, C. Kirches, S. Leyffer, J. Linderoth, J. Luedtke, and A. Mahajan. Mixed-integer nonlinear optimization. Acta Numerica, 22:1--131, 2013.
[5]
J. F. Benders. Partitioning procedures for solving mixed-variables programming problems. Numerische mathematik, 4(1):238--252, 1962.
[6]
V. D. Blondel, M. Esch, C. Chan, F. Clérot, P. Deville, E. Huens, F. Morlot, Z. Smoreda, and C. Ziemlicki. Data for development: the d4d challenge on mobile phone data. arXiv preprint arXiv:1210.0137, 2012.
[7]
N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi. Optimal geo-indistinguishable mechanisms for location privacy. In CCS, pages 251--262, 2014.
[8]
S. Boyd and L. Vandenberghe. Convex optimization. Cambridge university press, 2004.
[9]
Y. Chon, N. D. Lane, F. Li, H. Cha, and F. Zhao. Automatically characterizing places with opportunistic crowdsensing using smartphones. In UbiComp, pages 481--490, 2012.
[10]
C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos. Anonysense: privacy-aware people-centric sensing. In MobiSys, pages 211--224, 2008.
[11]
A. K. Dey. Understanding and using context. PUC, 5(1):4--7, 2001.
[12]
C. Dwork. Differential privacy: A survey of results. In TAMC, pages 1--19, 2008.
[13]
R. K. Ganti, F. Ye, and H. Lei. Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 49(11):32--39, 2011.
[14]
A. M. Geoffrion. Generalized benders decomposition. Journal of optimization theory and applications, 10(4):237--260, 1972.
[15]
B. Guo, Y. Liu, W. Wu, Z. Yu, and Q. Han. Activecrowd: A framework for optimized multitask allocation in mobile crowdsensing systems. IEEE THMS, 2016.
[16]
S. He, D.-H. Shin, J. Zhang, and J. Chen. Toward optimal allocation of location dependent tasks in crowdsensing. In INFOCOM, pages 745--753, 2014.
[17]
K.-R. Koch. Introduction to Bayesian statistics. Springer Science & Business Media, 2007.
[18]
S. Kullback and R. A. Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79--86, 1951.
[19]
A. H. Land and A. G. Doig. An automatic method of solving discrete programming problems. Econometrica: Journal of the Econometric Society, pages 497--520, 1960.
[20]
Y. Liu, B. Guo, Y. Wang, W. Wu, Z. Yu, and D. Zhang. Taskme: multi-task allocation in mobile crowd sensing. In UbiComp, 2016.
[21]
M. Mitchell. An introduction to genetic algorithms. MIT press, 1998.
[22]
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. ACM SIGMOD Record, 44(4):23--34, 2016.
[23]
L. Pournajaf, L. Xiong, V. Sunderam, and S. Goryczka. Spatial task assignment for crowd sensing with cloaked locations. In MDM, volume 1, pages 73--82, 2014.
[24]
R. K. Rana, C. T. Chou, S. S. Kanhere, N. Bulusu, and W. Hu. Ear-phone: an end-to-end participatory urban noise mapping system. In IPSN, pages 105--116, 2010.
[25]
X. Sheng, J. Tang, and W. Zhang. Energy-efficient collaborative sensing with mobile phones. In INFOCOM, pages 1916--1924, 2012.
[26]
H. To, G. Ghinita, and C. Shahabi. A framework for protecting worker location privacy in spatial crowdsourcing. Proc. VLDB Endowment, 7(10):919--930, 2014.
[27]
I. J. Vergara-Laurens, D. Mendez, and M. A. Labrador. Privacy, quality of information, and energy consumption in participatory sensing systems. In PerCom, pages 199--207, 2014.
[28]
L. Wang, D. Zhang, A. Pathak, C. Chen, H. Xiong, D. Yang, and Y. Wang. Ccs-ta: quality-guaranteed online task allocation in compressive crowdsensing. In UbiComp, pages 683--694, 2015.
[29]
L. Wang, D. Zhang, Y. Wang, C. Chen, X. Han, and A. M'hamed. Sparse mobile crowdsensing: challenges and opportunities. IEEE Communications Magazine, 54(7):161--167, 2016.
[30]
L. Wang, D. Zhang, D. Yang, B. Y. Lim, and X. Ma. Differential location privacy for sparse mobile crowdsensing. In ICDM, 2016.
[31]
L. Willenborg and T. De Waal. Elements of statistical disclosure control, volume 155. Springer Science & Business Media, 2012.
[32]
H. Xiong, D. Zhang, G. Chen, L. Wang, and V. Gauthier. Crowdtasker: Maximizing coverage quality in piggyback crowdsensing under budget constraint. In PerCom, pages 55--62, 2015.
[33]
H. Xiong, D. Zhang, L. Wang, and H. Chaouchi. Emc 3: Energy-efficient data transfer in mobile crowdsensing under full coverage constraint. IEEE TMC, 14(7):1355--1368, 2015.
[34]
D. Zhang, L. Wang, H. Xiong, and B. Guo. 4w1h in mobile crowd sensing. IEEE Communications Magazine, 52(8):42--48, 2014.
[35]
D. Zhang, H. Xiong, L. Wang, and G. Chen. Crowdrecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint. In UbiComp, pages 703--714, 2014.
[36]
Y. Zhu, Z. Li, H. Zhu, M. Li, and Q. Zhang. A compressive sensing approach to urban traffic estimation with probe vehicles. IEEE TMC, 12(11):2289--2302, 2013.

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Published In

cover image ACM Other conferences
WWW '17: Proceedings of the 26th International Conference on World Wide Web
April 2017
1678 pages
ISBN:9781450349130

Sponsors

  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Author Tags

  1. crowdsensing
  2. differential location privacy
  3. task allocation

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  • Research-article

Funding Sources

  • State Language Commission Key Program of China
  • Hong Kong ITF
  • ERC Consolidator
  • NSFC

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WWW '17
Sponsor:
  • IW3C2

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2025)$\mathsf {AVeCQ}$AVeCQ: Anonymous Verifiable Crowdsourcing With Worker QualitiesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.339634222:1(406-423)Online publication date: 1-Jan-2025
  • (2024)Improving Data Utility in Privacy-Preserving Location Data Collection via Adaptive Grid PartitioningElectronics10.3390/electronics1315307313:15(3073)Online publication date: 3-Aug-2024
  • (2024)Towards Efficient and Deposit-Free Blockchain-Based Spatial CrowdsourcingACM Transactions on Sensor Networks10.1145/365634320:3(1-22)Online publication date: 9-Apr-2024
  • (2024)RATE: Privacy-Preserving Task Assignment With Bi-Objective Optimization for Mobile CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2024.343958423:12(13851-13865)Online publication date: Dec-2024
  • (2024)RoPriv: Road Network-Aware Privacy-Preserving Framework in Spatial CrowdsourcingIEEE Transactions on Mobile Computing10.1109/TMC.2023.325523223:3(2351-2366)Online publication date: Mar-2024
  • (2024)Enhancing Sparse Mobile CrowdSensing With Manifold Optimization and Differential PrivacyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340766819(6070-6083)Online publication date: 2024
  • (2024)SecDR: Enabling Secure, Efficient, and Accurate Data Recovery for Mobile CrowdsensingIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326226821:2(789-803)Online publication date: Mar-2024
  • (2024)Employing discrete global grid systems for reproducible data obfuscationScientific Data10.1038/s41597-024-03354-511:1Online publication date: 17-May-2024
  • (2024)Preserving location privacy against inference attacks in indoor positioning systemPeer-to-Peer Networking and Applications10.1007/s12083-023-01609-317:2(784-799)Online publication date: 24-Jan-2024
  • (2024)pFind: Privacy-preserving lost object finding in vehicular crowdsensingWorld Wide Web10.1007/s11280-024-01300-427:5Online publication date: 13-Sep-2024
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