Privacy-Preserving Deep Learning Model for Decentralized VANETs Using Fully Homomorphic Encryption and Blockchain
Privacy-Preserving Deep Learning Model for Decentralized VANETs Using Fully Homomorphic Encryption and Blockchain
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使用全同态加密和区块链的去中心化 VANET 隐私保护深度学习模型
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Philip S. Yu
中科院分区：
文献类型：
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作者：
Jianguo Chen;Kenli Li;Philip S. Yu
在车载自组织网络中，网络传输和数据分析过程中的隐私保护和数据安全问题受到了广泛关注。在本文中，我们将深度学习，区块链和全同态加密（FHE）技术应用于VANESTO，并提出了一种分布式隐私保护深度学习（DPDL）模型。我们提出了一种分散式VANCITY（DVANCITY）架构，其中计算任务从集中式云服务分解到边缘计算（EC）节点，从而有效地减少网络通信开销和拥塞延迟。我们使用区块链在车辆、路边单元和EC节点之间建立安全可信的数据通信机制。此外，我们提出了一个DPDL模型提供隐私保护的数据分析DVANET，其中FHE算法被用来加密每个EC节点上的运输数据，并将其输入到本地DPDL模型，从而有效地保护隐私和车辆的可信度。此外，我们进一步使用区块链来提供一个分散和可信的DPDL模型更新机制，其中每个本地DPDL模型的参数都存储在区块链中，以便与其他分布式模型共享。通过这种方式，所有分布式模型都可以以可信和异步的方式更新其模型，避免可能的威胁和攻击。广泛的模拟进行评估的有效性，实用性和鲁棒性的建议DVANET系统和DPDL模型。
In Vehicular Ad-hoc Networks (VANETs), privacy protection and data security during network transmission and data analysis have attracted attention. In this paper, we apply deep learning, blockchain, and fully homomorphic encryption (FHE) technologies in VANETs and propose a Decentralized Privacy-preserving Deep Learning (DPDL) model. We propose a Decentralized VANETs (DVANETs) architecture, where computing tasks are decomposed from centralized cloud services to edge computing (EC) nodes, thereby effectively reducing network communication overhead and congestion delay. We use blockchain to establish a secure and trusted data communication mechanism among vehicles, roadside units, and EC nodes. In addition, we propose a DPDL model to provide privacy-preserving data analysis for DVANET, where the FHE algorithm is used to encrypt the transportation data on each EC node and input it into the local DPDL models, thereby effectively protecting the privacy and credibility of vehicles. Moreover, we further use blockchain to provide a decentralized and trusted DPDL model update mechanism, where the parameters of each local DPDL model are stored in the blockchain for sharing with other distributed models. In this way, all distributed models can update their models in a credible and asynchronous manner, avoiding possible threats and attacks. Extensive simulations are conducted to evaluate the effectiveness, practicality, and robustness of the proposed DVANET system and DPDL models.