微型电脑应用

为了保障混合区块链用户派发、交易和变更操作的安全，提出基于同态加密与机器学习的混合区块链隐私保护方法。以公钥压缩和换模为基础，采用同态加密方法，通过密钥生成、加密、密文运算、解密以及检验生成同态加密混合区块链，实现混合区块链数据加密，采用基于机器学习的卷积神经网络前向传播方法，依次通过卷积层、池化层、激励层、全局平均池化层操作完成同态加密结果检验，检验通过后，构建隐私混合区块链，使隐私数据密文上链，并在上链前排除无效数据，实现混合区块链保护。实验说明：该方法混合区块链数据加密效率及检验准确率高；隐私保护成功率接近100%,且不受节点数量增加影响；可保证混合区块链派发、交易和变更操作安全，有效避免用户隐私信息泄露。

In order to ensure the security of user distribution, transaction and change operations in hybrid blockchain, a hybrid blockchain privacy protection method based on homomorphic encryption and machine learning is proposed. Based on the public key compression and module transformation, the homomorphic encryption method is adopted to generate a homomorphic encryption hybrid blockchain through key generation, encryption, ciphertext operation, decryption and inspection, so as to realize the data encryption of the hybrid blockchain. The forward propagation method of convolutional neural network based on machine learning is adopted to complete the homomorphic encryption result verification through the operations of convolutional layer, pooling layer, incentive layer and global average pooling layer in turn. After passing the inspection, this paper builds a privacy hybrid blockchain to enable privacy data ciphertext to be linked, and eliminate invalid data before linking, so as to realize hybrid blockchain protection. Experimental results show that the hybrid blockchain data encryption method has high efficiency and inspection accuracy. The success rate of privacy protection is close to 100% and is not affected by the increase in the number of nodes. It can ensure the security of hybrid blockchain distribution, transaction and change operations, and effectively avoid the disclosure of users' privacy information.