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Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles

arXiv 2409.17287 ↗First posted 20 Sep 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 57 references that are in the map. First posted on arXiv 20 Sep 2024.

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References · 23

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11 more
  1. 2024Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6Gcited by 15
  2. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  3. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
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  11. 2023Learning Channel Capacity with Neural Mutual Information Estimator Based on Message Importance Measurecited by 2

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