This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A Secure and Privacy-Preserving Collaborative Machine Learning System for Intelligent Transportation System
Technical Paper
2020-01-0139
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
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.
Authors
Topic
Citation
Agrawal, 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
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 | ||
Unnamed Dataset 4 | ||
Unnamed Dataset 5 | ||
Unnamed Dataset 6 | ||
Unnamed Dataset 7 |
Also In
References
- Agrawal , V. 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 Abraham , A. , Mauri , J.L. , Buford , J.F. , Suzuki , J. , and Thampi , S.M. Advances in Computing and Communications Berlin, Heidelberg Springer 2011 644 653
- Li , T. , Sahu , A.K. , Talwalkar , A. , and Smith , V. 2019
- Samarakoon , S. , Bennis , M. , Saad , W. , and Debbah , M. 2018
- SAE Standars January 2014
- Xu , K. , Ding , H. , Guo , L. , and Fang , Y. A Secure Collaborative Machine Learning Framework Based on Data Locality 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 Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security, CCS ‘15 New York, NY 1322 1333 2015
- Melis , L. , Song , C. , De Cristofaro , E. , and Shmatikov , V. 2018
- Carlini , N. , Liu , C. , Kos , J. , Erlingsson , Ú. , and Song , D. 2018
- Melis , L. , Song , C. , Cristofaro , E.D. , and Shmatikov , V. Exploiting Unintended Feature Leakage in Collaborative Learning 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 Halevi , S. , Rabin , T. 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 Quisquater , J.-J. , Vandewalle , J. Advances in Cryptology - EUROCRYPT ‘89 Berlin, Heidelberg Springer 1990 302 319
- Hitaj , B. , Ateniese , G. , and Pérez-Cruz , F. 2017
- Mohassel , P. and Rindal , P. Aby3: A Mixed Protocol Framework for Machine Learning Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS ‘18 New York, NY 35 52 2018
- Moritz , P. , Nishihara , R. , Stoica , I. , and Jordan , M.I. 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. 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 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 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology London, UK, UK 255 270 2000
- Camenisch , J. and Groth , J. Group Signatures: Better Efficiency and New Theoretical Aspects Proceedings of the 4th international conference on Security in Communication Networks, SCN’04 Berlin, Heidelberg 2005 120 133
- Boneh , D. and Shacham , H. Group Signatures with Verifier-Local Revocation Proceedings of the 11th ACM Conference on Computer and Communications Security, CCS ‘04 New York, NY 2004 168 177
- Ateniese , G. , Song , D. , and Tsudik , G. Quasi-Efficient Revocation of Group Signatures Proceedings of the 6th International Conference on Financial Cryptography, FC’02 Berlin, Heidelberg 183 197 2003
- Chaum , D. and van Heyst , E. Group Signatures Advances in Cryptology - EUROCRYPT ‘91 Davies , D.W. Berlin, Heidelberg Springer 1991 257 265
- Boneh , D. , Lynn , B. , and Shacham , H. Short Signatures from the Weil Pairing Boyd , C. 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. https://github.com/vivek8705repo/CMLGS/tree/master/BBS
- De Caro , A. and Iovino , V. Jpbc: Java Pairing Based Cryptography Proceedings of the 16th IEEE Symposium on Computers and Communications, ISCC 2011 Kerkyra, Corfu, Greece 2011 850 855