@article {27386,
	title = {Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	year = {2020},
	pages = {4298 - 4311},
	publisher = { IEEE},
	abstract = {In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.},
	author = {Lu, Y. and Huang, X. and Zhang, K. and Maharjan, Sabita and Zhang, Yan}
}
@article {27379,
	title = {Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {16},
	year = {2020},
	month = {03/2020},
	pages = {2134 - 2143},
	publisher = { IEEE},
	abstract = {Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.},
	issn = {Print ISSN: 1551-3203 Electronic ISSN: 1941-0050},
	doi = {10.1109/TII.2019.2942179},
	url = {https://ieeexplore.ieee.org/document/8843942/authors$\#$authors},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {27380,
	title = {Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems},
	journal = {IEEE Network},
	volume = {34},
	year = {2020},
	month = {05/2020},
	pages = {50-56},
	publisher = { IEEE},
	abstract = {Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.},
	issn = {Print ISSN: 0890-8044 Electronic ISSN: 1558-156X},
	doi = {10.1109/MNET.011.1900317},
	url = {https://ieeexplore.ieee.org/document/9105934},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26979,
	title = {Blockchain and Federated Learning for Privacy-preserved Data Sharing in Industrial IoT},
	journal = { IEEE Transactions on Industrial Informatics (Early Access)},
	year = {2019},
	publisher = { IEEE},
	abstract = {The rapid increase in the volume of data generated from connected devices in Industrial Internet of Things (IIoT) paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g, data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first propose a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we transfer the data sharing problem into a machine learning problem by incorporating privacy-preserved federated learning. The privacy of data is well maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain thus the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency and enhanced security.},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26440,
	title = {Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks},
	journal = {IEEE Internet of Things Journal},
	year = {2018},
	month = {10/2018},
	publisher = {IEEE},
	abstract = {The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resource of vehicles, mobile edge computing integrated with vehicular networks gives rise to Vehicular Edge COmputing and Networks (VECONs) for providing powerful computing and massive storage resources. However, vehicular edge computing servers consisted of roadside units cannot be fully trusted, which may result in serious security and privacy challenges. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level security for data sharing in VECONs.},
	author = {Kang, J. and Yu, R. and Huang, X. and Yu, M. and Maharjan, Sabita and Xie, S. and Zhang, Yan}
}
@inproceedings {24549,
	title = {On-demand Pseudonym Systems in Geo-distributed Mobile Cloud Computing},
	journal = {IEEE International Conference on Cyber Security and Cloud Computing (IEEE CS Cloud): Best Paper Award},
	year = {2016},
	note = {Won the Best Paper Award (IEEE CSCloud 2016)},
	publisher = {IEEE},
	abstract = {Geo-distributed mobile cloud computing (GMCC) integrates location information into mobile cloud computing, that has high potential for a large variety of applications. In a vehicular environment, a GMCC provides a large number of resources to vehicles that are geographically close to them. However, there are few studies that focus on security and privacy pseudonym management in GMCC system for vehicular environment. We design a three-layer on-demand pseudonym system to manage the pseudonyms. Moreover, we propose a secure pseudonym distribution scheme for secure communication among vehicles. As the number of demanded pseudonyms varies with traffic loads in different clouds, we use a newsvendor model to address the optimal on-demand pseudonym distribution problem. Numerical results indicate our proposed schemes not only improve utility of the clouds, but also maximize utilization of pseudonyms.},
	author = {Kang, J. and Yu, R. and Huang, X. and Maharjan, Sabita and Zhang, Yan}
}
@article {24545,
	title = {Software Defined Networking with Pseudonym Systems for Secure Vehicular Clouds},
	journal = {IEEE Access},
	volume = {4},
	year = {2016},
	month = {04/2016},
	pages = {3522-3534},
	publisher = {IEEE},
	abstract = {The vehicular cloud is a promising new paradigm, where vehicular networking and mobile cloud computing are elaborately integrated to enhance the quality of vehicular information services. Pseudonym is a resource for vehicles to protect their location privacy, which should be efciently utilized to secure vehicular clouds. However, only a few existing architectures of pseudonym systems take exibility and efciency into consideration, thus leading to potential threats to location privacy. In this paper, we exploit software-dened networking technology to signicantly extend the exibility and programmability for pseudonym management in vehicular clouds. We propose a software-dened pseudonym system, where the distributed pseudonym pools are promptly scheduled and elastically managed in a hierarchical manner. In order to decrease the system overhead due to the cost of inter-pool communications, we leverage the two-sided matching theory to formulate and solve the pseudonym resource scheduling. We conducted extensive simulations based on the real map of San Francisco. Numerical results indicate that the proposed software-dened pseudonym system signicantly improves the pseudonym resource utilization, and meanwhile, effectively enhances the vehicles\&$\#$39; location privacy by raising their entropy.},
	issn = {2169-3536 },
	doi = {10.1109/ACCESS.2016.2560902},
	author = {Huang, X. and Yu, R. and Kang, J. and Wang, N. and Maharjan, Sabita and Zhang, Yan}
}
@article {23726,
	title = {Cooperative Resource Management in Cloud-enabled Vehicular Networks},
	journal = {IEEE Transactions on Industrial Electronics},
	volume = {62},
	year = {2015},
	month = {12/2015},
	pages = {7938-7951},
	publisher = {IEEE},
	author = {Yu, Rong and Huang, X. and Kang, Jiawen and Ding, Jiefei and Maharjan, Sabita and Gjessing, Stein and Zhang, Yan}
}
@article {23514,
	title = {MixGroup: Accumulative Pseudonym Exchanging for Location Privacy Enhancement in Vehicular Social Networks},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	volume = {13},
	year = {2015},
	month = {01/2016},
	pages = {93-105},
	publisher = {IEEE},
	author = {Yu, Rong and Kang, Jiawen and Huang, X. and Xie, Shengli and Zhang, Yan and Gjessing, Stein}
}
@inproceedings {24523,
	title = {An Optimal Replenishment Strategy in Energy Harvesting Wireless Networks with A Mobile Charger},
	journal = {QSHINE 2015 (Best Paper Award)},
	year = {2015},
	author = {Yu, R. and Huang, X. and Kang, J. and Yuen, C. and Vinel, A. and Jonsson, Magnus and Gjessing, Stein and Zhang, Yan}
}
