The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Enhancing Security and Privacy in Traffic-Monitoring Systems
Abstract
Intelligent transportation systems increasingly depend on probe vehicles to monitor traffic: they can automatically report position, travel time, traffic incidents, and road surface problems to a telematics service provider. This kind of traffic-monitoring system could provide good coverage and timely information on many more roadways than is possible with a fixed infrastructure such as cameras and loop detectors. This approach also promises significant reductions in infrastructure cost because the system can exploit the sensing, computing, and communications devices already installed in many modern vehicles. This architecture separates data from identities by splitting communication from data analysis. Data suppression techniques can help prevent data mining algorithms from reconstructing private information from anonymous database samples
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Citations
Cites
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Douceur, J. R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45748-8_24 · Cited by 4,419
An algorithm for tracking multiple targets
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Using GPS to learn significant locations and predict movement across multiple users
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The security of vehicular ad hoc networks
Raya, M., & Hubaux, J.-P. (2005) · Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks · doi:10.1145/1102219.1102223 · Cited by 978
Mix zones: user privacy in location-aware services
Beresford, A. R., & Stajano, F. (2004) · IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second · doi:10.1109/percomw.2004.1276918 · Cited by 485
Challenges in Securing Vehicular Networks
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Security Engineering: A Guide to Building Dependable Distributed Systems
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Protecting privacy in continuous location-tracking applications
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Self-citationOn the Anonymity of Periodic Location Samples
Gruteser, M., & Hoh, B. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-32004-3_19 · Cited by 160
Self-citationNeighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
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Cited by
Herrera, J. C., Work, D. B., Herring, R., Ban, X. (., Jacobson, Q., & Bayen, A. M. (2010) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2009.10.006 · Cited by 915
Shokri, R., Theodorakopoulos, G., Le Boudec, J.-Y., & Hubaux, J.-P. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.18 · Cited by 699
(2024) · Journal of Communications · doi:10.12720/jcm · Cited by 689
Metadata
- Authors
- Hoh, B., Gruteser, M., Xiong, H., & Alrabady, A.
- Venue
- IEEE Pervasive Computing
- Published
- 2006-10
- Volume
- 5
- Issue
- 4
- Pages
- 38-46
- ISSN
- 1536-1268 (ISSN-L)
- OpenAlex
- W2139650347
- Cited by
- 299
- References
- 16
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
