Method and device for jointly training service prediction model by two parties for protecting data privacy

Abstract

The embodiment of the invention provides a method and device for jointly training a service prediction model by two parties for protecting data privacy. The two parties respectively have a part of feature data. In the model iteration process, the two parties obtain encrypted fragments of the product result of the total feature matrix X and the total parameter matrix W through safety matrix multiplication; the two encrypted fragments are summarized by a second party with the label to obtain an encrypted product result Z; the second party obtains an encrypted error E based on the product resultZ and the encrypted label Y, and carries out secret sharing under homomorphic encryption. Therefore, the two parties respectively obtain error fragments. Then the two parties obtain corresponding gradient fragments through secret sharing and security matrix multiplication based on the error fragments and respective feature matrixes; and then, the first party updates the parameter fragments maintained by the first party by utilizing the gradient fragments of the first party, and the second party updates the parameter fragments maintained by the second party by utilizing the gradient fragments of the second party. Therefore, safe joint training for protecting data privacy is realized.

Description

technical field [0001] One or more embodiments of this specification relate to the fields of data security and machine learning, and in particular, relate to a method and an apparatus for jointly training a service prediction model by both parties. Background technique [0002] The data needed for machine learning often involves multiple fields. For example, in the merchant classification analysis scenario based on machine learning, the electronic payment platform has the transaction flow data of the merchants, the e-commerce platform stores the sales data of the merchants, and the banking institution has the loan data of the merchants. Data often exists in silos. Due to issues such as industry competition, data security, and user privacy, data integration faces great resistance. It is difficult to integrate data scattered across various platforms to train machine learning models. Under the premise of ensuring that data is not leaked, using multi-party data to jointly trai...

AI Technical Summary

Problems solved by technology

Due to issues such as industry competition, data security, and user privacy, data integration faces great resistance. It is difficult to integrate data scattered across various platforms to

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