Federated Learning-Based Collaborative Authentication Protocol for Shared Data in Social IoV

Zhao, Pengcheng; Huang, Yuanhao; Gao, Jianping; Xing, Ling; Wu, Honghai

doi:10.1109/jsen.2022.3153338

Cited by 26 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to benchmark approaches, the scheme exhibits higher authentication accuracy and better adaptability to high-speed IoV environments. An FL-assisted collaborative authentication protocol is introduced in [358] to guarantee integrity of messages exchanged among vehicles and resistance against various attack types, such as replay, Sybil and MitM. A communication-computation tradeoff analysis also reveals that the communication efficiency of the proposed solution can be drastically improved at the expense of a slightly increased computational cost.…”

Section: ) Authentication and Access Controlmentioning

confidence: 99%

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Sedar

Kalalas

Vázquez-Gallego

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.

show abstract

Section: ) Authentication and Access Controlmentioning

confidence: 99%

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Sedar

Kalalas

Vázquez-Gallego

et al. 2023

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…New schemes are proposed in the literature [16][17][18][19][20][21][22][23][24] for the authentication part of the data sharing process. Xuan et al 16 proposed an optimized scheme for cross-domain authentication between heterogeneous IoT applications using certificate-less public key cryptography and smart contract technology.…”

Section: Related Workmentioning

confidence: 99%

“…In the scheme, users share the same private public key pair and use the Canetti–Krawczyk (CK) secure mutual authentication protocol. To prevent data leakage and data propagation delay, Zhao et al 18 design a FL collaborative authentication protocol for shared data with low authentication delay. Efficient anonymous mutual authentication and key negotiation are implemented.…”

Section: Related Workmentioning

confidence: 99%

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

et al. 2023

View full text Add to dashboard Cite

SummaryFederated learning (FL) is widely used in internet of things (IoT) scenarios such as health research, automotive autopilot, and smart home systems. In the process of model training of FL, each round of model training requires rigorous decryption training and encryption uploading steps. The efficiency of FL is seriously affected by frequent encryption and decryption operations. A scheme of key computation and key management with high efficiency is urgently needed. Therefore, we propose a group key agreement technique to keep private information and confidential data from being leaked, which is used to encrypt and decrypt the transmitted data among IoT terminals. The key agreement scheme includes hidden attribute authentication, multipolicy access, and ciphertext storage. Key agreement is designed with edge‐cloud collaborative network architecture. Firstly, the terminal generates its own public and private keys through the key algorithm then confirms the authenticity and mapping relationship of its private and public keys to the cloud server. Secondly, IoT terminals can confirm their cryptographic attributes to the cloud and obtain the permissions corresponding to each attribute by encrypting the attributes. The terminal uses these permissions to encrypt the FL model parameters and uploads the secret parameters to the edge server. Through the storage of the edge server, these ciphertext decryption parameters are shared with the other terminal models of FL. Finally, other terminal models are trained by downloading and decrypting the shared model parameters for the purpose of FL. The performance analysis shows that this model has a better performance in computational complexity and computational time compared with the cited literature.

show abstract

“…Zhao et al 51 proposed a delay‐aware data leakage‐prevent model for the IoV. They designed a federated learning collaborative protocol for secure authentication of the shared data.…”

Section: Analysis Of Data‐centric Approaches In Iovmentioning

confidence: 99%

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Partovi

Zarei

Rahmani

2022

Int J Communication

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real-time data-centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data-centric approaches. This paper systematically categorizes the 48 recent articles on data-driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data-centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data-driven approaches in IoV. K E Y W O R D S data trading system, data traffic management, data transmission, Internet of Vehicles, IoV | INTRODUCTIONVehicular ad hoc networks (VANETs) as highly dynamic but well-organized emerging networks play an effective role in accelerating the improvement of the intelligent transportation systems (ITS) application. 1 Today ITS applications in many nations are expanding, whereby the rapid growth of networked sensors and context-aware computing systems, along with emerging communication technologies, have made the recognition of smart cities possible shortly. 2 The Internet of Things (IoT) technology has faced an increase in the number of dynamic objects, including connected vehicles, which formed the Internet of Vehicles (IoV), and made our lives easier, safer, and smarter. [3][4][5] VANETs, as the central infrastructure of the IoV ecosystem, aim to help drivers with possible inter-vehicle communication (IVC) and realtime data propagation even in high mobility, helping safety driving and receiving alerts for road hazardous. 6,7 Plug-in electric vehicles (PEVs) and unmanned aerial vehicles (UAVs), as an emerging part of energy-aware VANETs, have a significant impact on the formation of the IoV. 8 Connected vehicles in IoV are intelligent vehicles with a complicated software platform. Promoting vehicles from high dynamic nodes to intelligent vehicles with diverse communication

show abstract

Federated Learning-Based Collaborative Authentication Protocol for Shared Data in Social IoV

Cited by 26 publications

References 40 publications

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

A group key exchange and secure data sharing based on privacy protection for federated learning in edge‐cloud collaborative computing environment

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

Contact Info

Product

Resources

About