The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Framework for security and privacy in automotive telematics
Abstract
Automotive telematics may be defined as the information-intensive applications that are being enabled for vehicles by a combination of telecommunications and computing technology. Telematics by its nature requires the capture of sensor data, storage and exchange of data to obtain remote services. In order for automotive telematics to grow to its full potential, telematics data must be protected. Data protection must include privacy and security for end-users, service providers and application providers. In this paper, we propose a new framework for data protection that is built on the foundation of privacy and security technologies. The privacy technology enables users and service providers to define flexible data model and policy models. The security technology provides traditional capabilities such as encryption, authentication, non-repudiation. In addition, it provides secure environments for protected execution, which is essential to limiting data access to specific purposes.
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Cites
Building consumer trust online
Hoffman, D. L., Novak, T. P., & Peralta, M. (1999) · Communications of the ACM · doi:10.1145/299157.299175 · Cited by 2,132
A secure and reliable bootstrap architecture
Arbaugh, W. A., Farber, D. J., & Smith, J. M. (2002) · Proceedings. 1997 IEEE Symposium on Security and Privacy (Cat. No.97CB36097) · doi:10.1109/secpri.1997.601317 · Cited by 562
Platform for Enterprise Privacy Practices: Privacy-Enabled Management of Customer Data
Karjoth, G., Schunter, M., & Waidner, M. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-36467-6_6 · Cited by 221
Cited by
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
Gedik, B., & Liu, L. (2008) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2007.1062 · Cited by 837
Location Privacy in Mobile Systems: A Personalized Anonymization Model
Gedik, B., & Liu, L. (2005) · 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05) · doi:10.1109/icdcs.2005.48 · Cited by 719
AMOEBA: Robust Location Privacy Scheme for VANET
Sampigethaya, K., Li, M., Huang, L., & Poovendran, R. (2007) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2007.071007 · Cited by 391
Wireless Sensor Network Security: A Survey
Walters, J. P., Liang, Z., Shi, W., & Chaudhary, V. (2007) · Security in Distributed, Grid, Mobile, and Pervasive Computing · doi:10.1201/9780849379253-20 · Cited by 389
Protecting Location Privacy Through Path Confusion
Hoh, B., & Gruteser, M. (2006) · First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM'05) · doi:10.1109/securecomm.2005.33 · Cited by 386
Self-citation
Metadata
- Authors
- Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., & Tang, J.-M.
- Venue
- Proceedings of the 2nd international workshop on Mobile commerce
- Publisher
- ACM
- Published
- 2002-09-28
- Pages
- 25-32
- OpenAlex
- W2034809183
- Cited by
- 173
- References
- 3
- FWCI
- 4.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., & Tang, J.-M. (2002). Framework for security and privacy in automotive telematics. Proceedings of the 2nd international workshop on Mobile commerce. https://doi.org/10.1145/570705.570711
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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.
