Auditable privacy protection deep learning platform construction method based on block chain incentive mechanism

Abstract

Disclosed is a method for constructing an auditable and privacy-preserving collaborative deep learning platform based on a blockchain-empowered incentive mechanism, which allows trainers of multiple similar models to cooperate for training deep learning models while protecting confidentiality and auditing correctness of shared parameters. The invention has the following technical effects. Firstly, the encryption method used by model trainers protects the confidentiality of sharing parameters; furthermore, the updated parameters are decrypted through the cooperation of all participants, which reduces the possible disclosure of parameters. Secondly, the encrypted parameters are stored in the blockchain, and are only available to participants and authorized miners who are responsible to update parameters. Thirdly, the blockchain-based incentive mechanism guarantees the validity of the parameters, where collaborative trainers need to pay deposit when uploading parameters at the beginning and then the shared parameters can be validated. Concretely, if the parameters are invalid, the deposit would be forfeited.