School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
Sensors (Basel). 2022 Jul 29;22(15):5674. doi: 10.3390/s22155674.
In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack.
在物联网（IoT）中，通过网络编码进行的数据传输极易受到同代和跨代污染攻击。为了减轻此类攻击，先前的文献中采用了一些资源密集型的隐私保护方案。为了平衡资源消耗和数据隐私保护问题，提出了一种新颖的基于模糊的隐私保护方案。我们的方案构建在 T-S 模糊信任理论上，并通过设计的重复博弈模型来路由网络编码数据流到最佳簇中，以防御污染攻击。特别是，我们方案的安全性依赖于离散对数的难度。然后，我们证明设计的重复博弈模型具有子博弈完美纳什均衡，并且在数据安全的条件下，该模型可以提高资源利用效率。仿真结果表明，当数据包的长度大于 400 且迭代次数为 100 时，所提出的隐私保护方案的运行时间小于 1s，剩余能量高于 4J。因此，与先前的研究相比，我们的方案具有更高的时间和能量效率。此外，有效信任簇形成方案（ETCFS）可以在一种伪装攻击下更快地形成最佳簇。
