The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A framework for protecting worker location privacy in spatial crowdsourcing
Abstract
Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers , i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.
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Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Dwork, C. (2006) · Lecture Notes in Computer Science · doi:10.1007/11787006_1 · Cited by 5,291
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
The design and analysis of spatial data structures
Samet, H. (1989) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,380
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
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The new Casper: query processing for location services without compromising privacy
Mokbel, M. F., Chow, C.-Y., & Aref, W. G. (2006) · doi:10.5555/1182635.1164193 · Cited by 999
Smallest enclosing disks (balls and ellipsoids)
Welzl, E. (2006) · Lecture Notes in Computer Science · doi:10.1007/bfb0038202 · Cited by 791
Private queries in location based services: anonymizers are not necessary
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Self-citationDifferentially private recommender systems: Building privacy into the Netflix Prize contenders
McSherry, F., & Mironov, I. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557090 · Cited by 664
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Cited by
Crowdsourced Data Management: A Survey
Li, G., Wang, J., Zheng, Y., & Franklin, M. J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2535242 · Cited by 283
A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles
Zhang, J., Yang, F., Ma, Z., Wang, Z., Liu, X., & Ma, J. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3010288 · Cited by 98
Metadata
- Authors
- To, H., Ghinita, G., & Shahabi, C.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2014-06
- Volume
- 7
- Issue
- 10
- Pages
- 919-930
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2196824873
- Cited by
- 361
- References
- 23
- FWCI
- 35.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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