强鲁棒性边缘计算数据安全算法

Strongly Robust Data Security Algorithms for Edge Computing

针对由于采用分布式部署传感器的方式会导致边缘服务器出现数据量分布单一且不平衡的现象,并且边缘计算下的模型训练也会因梯度异常导致数据集被污染带来极为严重的隐私泄露问题,提出了强鲁棒性边缘计算(EC:Edge Computing)数据安全算法(RDSEC:Strongly Robust Data Security Algorithms for Edge Computing)。该算法利用异构网络对图像特征共同提取,并使用同态加密实现对边缘服务器的参数加密,实现隐私保护。若在边缘节点进行梯度异常检测时发现异常,则边缘节点上传梯度时会附加一个信号告知云中心当前边缘节点所上传的参数是否可用。该算法在Cifar10和Fashion数据集上的实验取得了极佳结果,实现了数据集分配比失衡的条件下也能有效聚合边缘服务器参数,提升边缘节点算力和运算准确率。并且在保证数据隐私的条件下,使模型的鲁棒性、精确性和训练速度大幅提升,实现边缘节点对图像预测的高准确率。

The use of distributed deployment of sensors will lead to the edge of the server data distribution single imbalance phenomenon,the model training under edge computing can also result in serious privacy leakage problem due to the data set pollution caused by gradient anomaly.RDSEC(Strongly Robust Data Security Algorithms for Edge Computing)is proposed,encryption algorithm is used to encrypt the parameters of the edge server to protect privacy.If an anomaly is found in the gradient anomaly detection of the edge node,the edge node uploads the gradient with a signal to tell the cloud center if the current parameters uploaded by the edge node are available.The experimental results on CIFAR10 and Fashion data sets show that the algorithm can efficiently aggregate the parameters of edge servers and improve the computing power and accuracy of edge nodes.Under the condition of ensuring data privacy,the robustness,accuracy and training speed of the model are greatly improved,and the high accuracy of edge node is achieved.