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A Secure and Privacy-Preserving Collaborative Machine Learning System for Intelligent Transportation System
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Modern vehicles are increasingly equipped with multiple advanced on-board sensors and keep generating large volumes of data. Along with the recent advances in a wide range of Machine Learning (ML) algorithms, the vehicular data are being analyzed intelligently to enable users to be better informed and make safer, more coordinated, and smarter use of transport networks. The success of ML model relies on the availability of large set of relevant data so that the underlying model can be trained better. However, it is not possible for a ML model to fetch the complete set of data from a single vehicle, thus, the collaboration of other vehicles are desired in sharing their local model and collaboratively training the model. Collaborative machine learning (CML) mechanism can improve the intelligence of the ML models in different vehicles by transferring the learned knowledge from the local ML model of one vehicle to another across the distributed network. However, the privacy concerns related to sharing the ML models often create hindrance towards sharing the local model with others. Sharing the information related to the local model may also leak sensitive data/patterns pertaining to the vehicles user. The aim of the paper is to discuss the privacy issues specific to sharing ML model in the collaborative ML scenarios, and propose the use of group signature scheme to enable security and privacy. The security and performance of the proposed system based on the group signature scheme is evaluated for the intelligent transportation systems.
CitationAgrawal, V., Ansari, A., and D H, S., "A Secure and Privacy-Preserving Collaborative Machine Learning System for Intelligent Transportation System," SAE Technical Paper 2020-01-0139, 2020, https://doi.org/10.4271/2020-01-0139.
Data Sets - Support Documents
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- Agrawal, V. , “Performance Evaluation of Group Signature Schemes in Vehicular Communication: a Feasibility Study for Vehicular Communication,” 2012.
- Zhang, J., Wang, F., Wang, K., Lin, W. et al. , “Data-Driven Intelligent Transportation Systems: A Survey,” IEEE Transactions on Intelligent Transportation Systems 12:1624-1639, Dec 2011.
- Dimitrakopoulos, G. and Demestichas, P. , “Intelligent Transportation Systems,” IEEE Vehicular Technology Magazine 5:77-84, March 2010.
- Grover, J., Prajapati, N.K., Laxmi, V., and Gaur, M.S. , “Machine Learning Approach for Multiple Misbehavior Detection in Vanet,” . In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., and Thampi, S.M., editors. Advances in Computing and Communications, (Berlin, Heidelberg, Springer, 2011), 644-653.
- Li, T., Sahu, A.K., Talwalkar, A., and Smith, V. , “Federated Learning: Challenges, Methods, and Future Directions,” 2019.
- Samarakoon, S., Bennis, M., Saad, W., and Debbah, M. , “Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications,” CoRR, abs/1807.08127, 2018.
- SAE Standars , “Taxonomy and Definitions for Terms Related to on-Road Motor Vehicle Automated Driving Systems,” January 2014.
- Xu, K., Ding, H., Guo, L., and Fang, Y. , “A Secure Collaborative Machine Learning Framework Based on Data Locality,” in 2015 IEEE Global Communications Conference (GLOBECOM), Dec. 1-5, 2015.
- Fredrikson, M., Jha, S., and Ristenpart, T. , “Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures,” in Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security, CCS ‘15, New York, NY, 1322-1333, 2015, ACM.
- Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V. , “Exploiting Unintended Feature Leakage in Collaborative Learning,” arXiv preprint arXiv:1805.04049, 2018.
- Carlini, N., Liu, C., Kos, J., Erlingsson, Ú., and Song, D. , “The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets,” CoRR, abs/1802.08232, 2018.
- Melis, L., Song, C., Cristofaro, E.D., and Shmatikov, V. , “Exploiting Unintended Feature Leakage in Collaborative Learning,” in 2019 IEEE Symposium on Security and Privacy (SP), 691-706, 2018.
- Kerber, W. , “Data Governance in Connected Cars: The Problem of Access to in-Vehicle Data,” J. Intell. Prop. Info. Tech. & Elec. Com. L. 9:310, 2018.
- Hornung, G. and Goeble, T. , ““Data Ownership” Im Vernetzten Automobil,” Computer und Recht 31(4):265-273, 2015.
- Dwork, C., McSherry, F., Nissim, K., and Smith, A. , “Calibrating Noise to Sensitivity in Private Data Analysis,” . In: Halevi, S., Rabin, T., editors. Theory of Cryptography. (Berlin, Heidelberg, Springer, 2006), 265-284.
- Dwork, C. and Roth, A. , “The Algorithmic Foundations of Differential Privacy,” Found. Trends Theor. Comput. Sci. 9:211-407, Aug. 2014.
- Dwork, C. , “A Firm Foundation for Private Data Analysis,” Commun. ACM 54:86-95, Jan. 2011.
- Waidner, M. , “Unconditional Sender and Recipient Untraceability in Spite of Active Attacks,” . In: Quisquater, J.-J., Vandewalle, J., editors. Advances in Cryptology - EUROCRYPT ‘89. (Berlin, Heidelberg, Springer, 1990), 302-319.
- Hitaj, B., Ateniese, G., and Pérez-Cruz, F. , “Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning,” CoRR, abs/1702.07464, 2017.
- Mohassel, P. and Rindal, P. , “Aby3: A Mixed Protocol Framework for Machine Learning,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS ‘18, New York, NY, 35-52, 2018, ACM.
- Moritz, P., Nishihara, R., Stoica, I., and Jordan, M.I. , “Sparknet: Training Deep Networks in Spark,” CoRR, abs/1511.06051, 2015.
- Xing, E., Ho, Q., Dai, W., Kim, J.K. et al. , “Petuum: A New Platform for Distributed Machine Learning on Big Data,” IEEE Transactions on Big Data 1:49-67, June 2015.
- Hard, A., Rao, K., Mathews, R., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D. , “Federated Learning for Mobile Keyboard Prediction,” ArXiv, abs/1811.03604, 2018.
- Yang, Q., Liu, Y., Chen, T., and Tong, Y. , “Federated Machine Learning: Concept and Applications,” ACM Trans. Intell. Syst. Technol. 10:12:1-12:19, Jan. 2019.
- Boneh, D., Boyen, X., and Shacham, H. , “Short Group Signatures,” Proceedings of CRYPTO ‘04, LNCS Series (Springer-Verlag, 2004), 41-55.
- Malina, L., Vives-Guasch, A., Castellà-Roca, J., Viejo, A., and Hajny, J. , “Efficient Group Signatures for Privacy-Preserving Vehicular Networks,” Telecommunication Systems 58:293-311, Apr 2015.
- Ateniese, G., Camenisch, J., Joye, M., and Tsudik, G. , “A Practical and Provably Secure Coalition-Resistant Group Signature Scheme,” in Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology, CRYPTO ‘00, (London, UK, UK), 255-270, Springer-Verlag, 2000.
- Camenisch, J. and Groth, J. , “Group Signatures: Better Efficiency and New Theoretical Aspects,” in Proceedings of the 4th international conference on Security in Communication Networks, SCN’04, Berlin, Heidelberg, 2005, 120-133, Springer-Verlag.
- Boneh, D. and Shacham, H. , “Group Signatures with Verifier-Local Revocation,” in Proceedings of the 11th ACM Conference on Computer and Communications Security, CCS ‘04, New York, NY, 2004, 168-177, ACM.
- Ateniese, G., Song, D., and Tsudik, G. , “Quasi-Efficient Revocation of Group Signatures,” in Proceedings of the 6th International Conference on Financial Cryptography, FC’02, Berlin, Heidelberg, 183-197, Springer-Verlag, 2003.
- Chaum, D. and van Heyst, E. , “Group Signatures,” in Advances in Cryptology - EUROCRYPT ‘91 (Davies, D.W., ed.), (Berlin, Heidelberg: Springer, 1991), 257-265.
- Boneh, D., Lynn, B., and Shacham, H. , “Short Signatures from the Weil Pairing,” . In: Boyd, C., editor. Advances in Cryptology - ASIACRYPT 2001. (Berlin, Heidelberg, Springer, 2001), 514-532.
- Lin, X., Sun, X., Ho, P., and Shen, X. , “Gsis: A Secure and Privacy-Preserving Protocol for Vehicular Communications,” IEEE Transactions on Vehicular Technology 56:3442-3456, Nov 2007.
- Agrawal, V. , “Performance Evaluation Code for Group Signature Schemes,” https://github.com/vivek8705repo/CMLGS/tree/master/BBS.
- De Caro, A. and Iovino, V. , “Jpbc: Java Pairing Based Cryptography,” in Proceedings of the 16th IEEE Symposium on Computers and Communications, ISCC 2011, Kerkyra, Corfu, Greece, June 28-July 1, 2011, 850-855, IEEE.