Abstract: Machine learning has been applied in more and more scenarios,but software that employs machine learning to perform tasks depends on third-party to update the models.This paper proposes and implements a model chain by utilizing computation power of training neural network consumption with proof-of-work.As a blockchain that can be used to share data and machine learning models,the data shared anonymously by the whole network node are used in the model chain,and the neural network model is explored based on the primary network,thus realizing neural network model update without relying on the third-party.The shared data are signed with a ring signature to protect local data privacy.The whole network uses the same test set to evaluate the model,and the adopted model can be regarded as proof-of-work.This paper proposes two reward mechanisms,i.e.,material reward and model reward.To deal with potential threats,e.g.,blockchain ledger analysis,dirty data attacks and fraudulent voting,this paper proposes ideal ring signature scheme and several solutions.Finally,extensive experiments on real data are conducted,and the results show that the model in the model chain can adapt to the user changes and data changes.

Key words: Blockchain, Data Sharing, Neural network, Proof-of-work, Voting consensus mechanism