Abstract: Voice data processing for voice intention understanding includes performing voice data feature extraction on voice data to obtain first feature data. Local frequency domain transform processing is performed on the first feature data to obtain at least one feature map including a plurality of elements and corresponding to one data block in the first feature data, each element corresponding to one frequency in a frequency domain. Constructing, by separately using an element corresponding to each frequency in the at least one feature map a frequency component channel feature map corresponding to each frequency. Selecting at least one target frequency component channel feature map from the constructed frequency component channel feature maps to obtain desensitized feature data of the voice data, where the selected target frequency component feature map is a critical channel feature for voice intention understanding and is a non-critical channel feature for voice signal recognition.

Abstract: Implementations of the present specification provide an image data processing method, an image data recognition method, a training method for an image recognition model, an image data processing apparatus, an image data recognition apparatus, and a training apparatus for an image recognition model. During image data processing, data anonymization processing is performed on image data based on frequency domain transform to obtain anonymized image data of the image data. The obtained anonymized image data includes a subgraph data set. Each subgraph data in the subgraph data set corresponds to a different frequency. Then, image blending processing is performed on the obtained anonymized image data based on data augmentation, to obtain augmented anonymized image data.

Abstract: Embodiments of this specification provide privacy-preserving multi-party joint model prediction methods, systems, and apparatuses. A computing device converts all nonlinear activation functions in a service prediction model into corresponding linear activation functions, where any nonlinear activation function is converted into linear functions of several segments, any linear function is obtained by performing Taylor expansion on the nonlinear activation function at a Taylor expansion point in a corresponding segment, and in the segment, a difference between the linear function and the nonlinear activation function is less than a preset threshold.

Abstract: Implementations of the present specification provide an image data processing method, an image data recognition method, a training method for an image recognition model, an image data processing apparatus, an image data recognition apparatus, and a training apparatus for an image recognition model. During image data processing, data anonymization processing is performed on image data based on frequency domain transform to obtain anonymized image data of the image data. The obtained anonymized image data includes a subgraph data set. Each subgraph data in the subgraph data set corresponds to a different frequency. Then, image blending processing is performed on the obtained anonymized image data based on data augmentation, to obtain augmented anonymized image data.

Abstract: Voice data processing for voice intention understanding includes performing voice data feature extraction on voice data to obtain first feature data. Local frequency domain transform processing is performed on the first feature data to obtain at least one feature map including a plurality of elements and corresponding to one data block in the first feature data, each element corresponding to one frequency in a frequency domain. Constructing, by separately using an element corresponding to each frequency in the at least one feature map a frequency component channel feature map corresponding to each frequency. Selecting at least one target frequency component channel feature map from the constructed frequency component channel feature maps to obtain desensitized feature data of the voice data, where the selected target frequency component feature map is a critical channel feature for voice intention understanding and is a non-critical channel feature for voice signal recognition.

Abstract: Implementations of the present specification provide an image recognition model training method and apparatus. Each first member device performs data desensitization processing on training sample image data based on frequency domain transform to obtain first desensitized image data; provides the first desensitized image data to a hyperparameter selection model to select a first hyperparameter; performs image mixing processing on the first desensitized image data based on data augmentation by using the first hyperparameter to obtain second desensitized image data and second label data that is label mixing processed and corresponding to the second desensitized image data; and then trains an image recognition model by using the second desensitized image data and the second label data. A second member device updates the image recognition model by using a model training result received from each first member device.

Abstract: A computer-implemented method, medium, and system are disclosed. One example computer-implemented method performed by a server includes obtaining training task information from a task party. The training task information includes information about a to-be-pretrained model and information about a to-be-trained target model. A respective task acceptance indication from each of at least one of a plurality of data parties is received to obtain a candidate data party set. The information about the to-be-pretrained model is sent to each data party in the candidate data party set. A respective pre-trained model of each data party is received. A respective performance parameter of the respective pre-trained model of each data party is obtained. One or more target data parties from the candidate data party set is determined. The information about the to-be-trained target model is sent to the one or more target data parties to obtain a target model.

Abstract: A computer-implemented method, medium, and system are disclosed. One example computer-implemented method performed by a server includes obtaining training task information from a task party. The training task information includes information about a to-be-pretrained model and information about a to-be-trained target model. A respective task acceptance indication from each of at least one of a plurality of data parties is received to obtain a candidate data party set. The information about the to-be-pretrained model is sent to each data party in the candidate data party set. A respective pre-trained model of each data party is received. A respective performance parameter of the respective pre-trained model of each data party is obtained. One or more target data parties from the candidate data party set is determined. The information about the to-be-trained target model is sent to the one or more target data parties to obtain a target model.