Privacy Protection Method of Distributed Encrypted Traffic Classification Based on Homomorphic Encryption
With the rapid development of information technology,the amount of data has increased rapidly,and the distributed storage methods have gradually evolved.To solve the problem that the distributed data storage mode is prone to gradient leakage caused by gradient inference attacks on model training,which in turn leads to the leakage of datasets in distributed nodes,a privacy protection method of distributed encrypted traffic classification(Pa-Fed)based on homomorphic encryption algorithm is proposed.After the distributed nodes are trained,the local model pass-es the parameters to the central server through Paillier homomorphic encryption.When the parameters are aggregated on the central server,the ciphertext state of the parameters is maintained to ensure privacy during transmission.The experiment can well maintain the classification accuracy rate,and carry out the gradient inference attack on the dis-tributed node data after encryption,which effectively verifies the privacy of distributed node data.
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