Privacy Protection of Big Data in Complex Network Based on Federated Learning Algorithm
Network data contains a lot of data information.In order to avoid leaking users'privacy information and effectively ensure the security of complex network big data,this paper presented a privacy protection method for com-plex network big data based on federated learning algorithm.Firstly,federated learning algorithm was combined with VAE technology to build a variational autoencoder for training local data sets.Then,parameter aggregation chain was constructed through the trained data.Meanwhile,the intermediate parameters generated in the process of training were set as evidence.Moreover,model parameters were verified by using violent nodes.After that,false and low-quality participating nodes were deleted.Furthermore,a joint model was built.Then,the direct exchange of data was replaced with the interaction of model parameters,thus completing the privacy protection of complex network big data.The ex-perimental results show that the proposed method can effectively protect big data privacy while improving the operation efficiency and resistance to virus attacks,thus reducing the average storage loss.
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