The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Virtual trip lines for distributed privacy-preserving traffic monitoring
Abstract
Automotive traffic monitoring using probe vehicles with Global Positioning System receivers promises significant improvements in cost, coverage, and accuracy. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we propose a system based on virtual trip lines and an associated cloaking technique. Virtual trip lines are geographic markers that indicate where vehicles should provide location updates. These markers can be placed to avoid particularly privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus they facilitate the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS smartphone clients and conducted a controlled experiment with 20 phone-equipped drivers circling a highway segment. Results show that even with this low number of probe vehicles, travel time estimates can be provided with less than 15% error, and applying the cloaking techniques reduces travel time estimation accuracy by less than 5% compared to a standard periodic sampling approach.
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Dingledine, R., Mathewson, N., & Syverson, P. (2004) · doi:10.21236/ada465464 · Cited by 4,104
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
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
Self-citationPrivacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712
A survey of physics-based attack detection in cyber-physical systems
Giraldo, J., Urbina, D., Cardenas, A., Valente, J., Faisal, M., Ruths, J., Tippenhauer, N. O., Sandberg, H., & Candell, R. (2019) · ACM Computing Surveys · doi:10.1145/3203245 · Cited by 535
Sell your experiences: a market mechanism based incentive for participatory sensing
Lee, J.-S., & Hoh, B. (2010) · 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom.2010.5466993 · Cited by 396
Self-citationSmartphone-based real time vehicle tracking in indoor parking structures
Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., & Luo, G. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2684167 · Cited by 48
Metadata
- Authors
- Hoh, B., Gruteser, M., Herring, R., Ban, J., Work, D., Herrera, J.-C., Bayen, A. M., Annavaram, M., & Jacobson, Q.
- Venue
- Proceedings of the 6th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2008-06-17
- Pages
- 15-28
- OpenAlex
- W2126236329
- Cited by
- 351
- References
- 2
- FWCI
- 29.28
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hoh, B., Gruteser, M., Herring, R., Ban, J., Work, D., Herrera, J.-C., Bayen, A. M., Annavaram, M., & Jacobson, Q. (2008). Virtual trip lines for distributed privacy-preserving traffic monitoring. Proceedings of the 6th international conference on Mobile systems, applications, and services. https://doi.org/10.1145/1378600.1378604
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