Abstract: The present disclosure relates to a data protection method, an apparatus, a medium and a device. The method includes: acquiring gradient association information respectively corresponding to reference samples of a target batch of an active party of a joint training model; according to the proportion occupied respectively by reference samples of positive examples and reference samples of negative examples in all reference samples of the target batch, determining a constraint condition of the data noise to be added; determining information of said data noise according to the gradient association information and the constraint condition corresponding to the reference samples; correcting, according to the information of said data noise, an initial gradient transmission value corresponding to each reference sample, so as to obtain target gradient transmission information; and sending the target gradient transmission information to a passive party of the joint training model.

Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

Abstract: Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

Abstract: Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.