The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

中文翻译：

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6G 社区分段车载社交网络中的安全数据共享方案


使用空中基站、人工智能云和卫星存储可以帮助管理车辆社交网络的位置、流量和基于特定应用的服务。然而，此类数据的共享使得车载网络容易遭受数据和隐私泄露。在这方面，本文提出了一种使用社区分段的高效、安全的数据共享方案和基于区块链的车辆社交网络框架。所提出的工作考虑了利用结构相似性、模块化矩阵和数据兼容性的动态性的相似性矩阵。然后，这些相似性矩阵通过堆叠的自动编码器，这些自动编码器经过训练以提取编码的嵌入。然后采用基于密度的聚类方法根据编码嵌入之间的信息距离找到社区片段。还采用基于Hyperledger Fabric平台的区块链网络来保证数据共享安全。我们进行了大量的实验，从误差平方和、共享程度、时间成本、计算复杂度、吞吐量和 CPU 利用率等方面评估所提出的数据共享框架，以证明其有效性和适用性。结果表明，CSB框架实现了更高程度的SD、更低的计算复杂度和更高的吞吐量。