This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Secure and Privacy-Preserving Data Collection Mechanisms for Connected Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Nowadays, the automotive industry is experiencing the advent of unprecedented applications with connected devices, such as identifying safe users for insurance companies or assessing vehicle health. To enable such applications, driving behavior data are collected from vehicles and provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers). In the new wave of IoT (Internet of Things), driving statistics and users’ data generated from wearable devices can be exploited to better assess driving behaviors and construct driver models. We propose a framework for securely collecting data from multiple sources (e.g., vehicles and brought-in devices) and integrating them in the cloud to enable next-generation services with guaranteed user privacy protection. To achieve this goal, we design fine-grained privacy-aware data collection and upload policies that balance between enforcing privacy requirements and optimizing resource consumption (e.g., processing, network bandwidth). The optimal policy will be determined by the privacy index of the integrated multi-source data to be used by the specific service and the desired resource usage. Real-world experiments and privacy leakage analysis are conducted to address privacy issues in vehicle data collection and integration, raise public awareness around privacy leakage, and validate the proposed system.
CitationLi, H., Ma, D., Medjahed, B., Wang, Q. et al., "Secure and Privacy-Preserving Data Collection Mechanisms for Connected Vehicles," SAE Technical Paper 2017-01-1660, 2017, https://doi.org/10.4271/2017-01-1660.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Wagner, S. Brandt T., and Neumann, D.“Data Analytics in Free-Floating Carsharing: Evidence from the City of Berlin,”” In 48th Hawaii International Conference on System Sciences (HICSS), pp. 897–907, IEEE, 2015.
- Enev M., Takakuwa A., Koscher K., and Kohno T., “Automobile Driver Fingerprinting,”” Proceedings on Privacy Enhancing Technologies, 2016(1), 34–50, 2016.
- Michalevsky Y., Schulman A., Veerapandian G. A., Boneh D., and Nakibly G., Powerspy: Location tracking using mobile device power analysis. In 24th USENIX Security Symposium (USENIX Security 15), pp. 785–800, (2015).
- Das A., Borisov N., and Caesar M. “Tracking Mobile Web Users Through Motion Sensors: Attacks and Defenses,” In Proceedings of the 23rd Annual Network and Distributed System Security Symposium (NDSS), 2016.
- Narain S., Vo-Huu T. D., Block K., and Noubir G., “Inferring User Routes and Locations using Zero-Permission Mobile Sensors,” IEEE Symposium on Security and Privacy, 2016.
- Li H., Zhu H., Du S., Liang X., and Shen X. “Privacy Leakage of Location Sharing in Mobile Social Networks: Attacks and Defense,”” IEEE Transactions on Dependable and Secure Computing, 2016, doi:10.1109/TDSC.2016.2604383.
- Li H., Xu Z., Zhu H., Ma D., Li S., and Xing K., “Demographics Inference Through Wi-Fi Network Traffic Analysis”, In INFOCOM, IEEE, 2016.
- Excecutive Office of the President, ”Big data: Seizing opportunities, preserving values”, Feb. 2015.
- MacKie-Mason Jef. ”Can we afford privacy from surveillance?”, IEEE Security and Privacy, Vol. 12, No. 5, pp.86–89, 2014.
- Masoumzadeh, A., Joshi, J.“Top Location Anonymization for Geosocial Network Datasets,” Transactions on Data Privacy, 6(1), 107–126, 2013.
- Markwood I. D., and Liu Y., “Vehicle Self-Surveillance: Sensor-Enabled Automatic Driver Recognition,” In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (pp. 425–436), ACM, 2016.
- Kifayat K., Merabti M., Shi Q., and Llewellyn-Jones D. “Applying secure data aggregation techniques for a structure and density independent group based key management protocol,” In Third International Symposium on Information Assurance and Security (pp. 44–49). IEEE, 2007.
- Dwork C., “Differential privacy,” In” Encyclopedia of Cryptography and Security (pp. 338–340). Springer US, 2011.
- Dwork, C.“Differential privacy: A survey of results,” In International Conference on Theory and Applications of Models of Computation (pp. 1–19). Springer Berlin Heidelberg, 2008.
- Acquisti A., Brandimarte L., and Loewenstein G. “Privacy and human behavior in the age of information”, In Science, vol. 347, no. 6621, pp. 509–514, 2015.
- Landau S., “Control use of data to protect privacy,” In Science, 347(6221), 504–506, 2015.
- Doerzaph, Z., Dingus, T., and Hankey, J. "Improving Driver Safety through Naturalistic Data Collection and Analysis Methods," SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 3(2):162–169, 2010, doi:10.4271/2010-01-2333.
- Peterson, J. "Vehicle Field Data Collection," SAE Technical Paper 740941, 1974, doi:10.4271/740941.
- Moore, T., Fisher, J., Heller, M., Lau, E. , "Occupant Injury in Motor Vehicle Collisions: Using Field Accident Data from Multiple Sources," SAE Technical Paper 2009-01-0394, 2009, doi:10.4271/2009-01-0394.
- Paar, C., Rupp, A., Schramm, K., Weimerskirch, A. , "Implementing Data Security and Privacy in Next-Generation Electric Vehicle Systems," SAE Technical Paper 2010-01-0743, 2010, doi:10.4271/2010-01-0743.
- Tang J., Cui Y., Li Q., Ren K., Liu J., and Buyya R., “Ensuring Security and Privacy Preservation for Cloud Data Services,” ACM Computing Surveys, 49(1), 13, 2016.
- Gentry C. “Fully homomorphic encryption using ideal lattices,” In Proceedings of ACM Symposium on Theory of Computing, Vol. 9. ACM, 169178, 2009.
- Wang C., Cao N., Li J., Ren K., and Lou W. “Secure ranked keyword search over encrypted cloud data,” In Proceedings of IEEE 30th International Conference on Distributed Computing Systems, 253262, 2010.
- Vimercati S., Foresti S., Jajodia S., Paraboschi S., Pelosi G., and Samarati P. “Encryption-based policy enforcement for cloud storage,” In Proceedings of IEEE 30th International Conference on Distributed Computing Systems Workshops, IEEE, 4251, 2010.
- Chor B., Kushilevitz E., Goldreich O., and Sudan M. “Private information retrieval,” Journal of the ACM (JACM) 45, 6 (1998), 965981, 1998.
- Ding X., Yang Y., and Deng R. H. “Database access pattern protection without fullshuffles,” IEEE Transactions on Information Forensics and Security (TIFS) 6, 1, 189201, 2011.
- Yang K., Zhang J., Zhang W., and Qiao D. “A lightweight solution to preservation of access pattern privacy in untrusted clouds,” In Computer Security (ES-ORICS11). Springer, 528547, 2011.
- Sun W., Wang B., Cao N., Li M., Lou W., Hou Y. T., and Li H. “Privacy preserving multi-keyword text search in the cloud supporting similarity-based ranking,” In Proceedings of the 8th ACM Symposium on Information, Computer and Communications Security (ASI-ACCS13), ACM, 7182, 2013.
- Cao N., Wang C., Li M., Ren K., and Lou W. “Privacy-preserving multi-keyword ranked search over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems 25, 1, 222233, 2014.
- Sahai A. and Waters B. “Fuzzy identity-based encryption,” In Advances in Cryptology (EURO-CRYPT05). Springer, 457473, 2005.
- Li L., Zhao X., Xue G. “Unobservable Re-authentication for Smartphones,” In NDSS, 2013.
- Wang X., Sun K., Wang Y., Jing J. DeepDroid: Dynamically Enforcing Enterprise Policy on Android Devices, In NDSS, 2015.