Privacy-preserving graphical model training methods, apparatuses, and devices

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• acquiring, by a terminal device, node information of a first graph to be constructed, node information of a second graph and node connection information of the second graph;
• constructing, by the terminal device, the first graph by using a predetermined fully connected network based on the node information of the first graph;
• constructing the second graph by using a predetermined graph network based on the node information and the node connection information of the second graph, wherein the predetermined fully connected network and the predetermined graph network have same network parameters;
• acquiring, by the terminal device, a latent vector of a first node that has training label information in the first graph;
• constructing, by the terminal device, first sample data by using a predetermined InstaHide privacy protection rule based on the latent vector of the first node and the training label information corresponding to the first node;
• generating, by the terminal device, training label information for a second node without training label information in the first graph and a node in the second graph;
• acquiring, by the terminal device, latent vectors of the second node and the node in the second graph;
• constructing, by the terminal device, second sample data by using the predetermined InstaHide privacy protection rule based on the latent vectors of the second node, the node in the second graph, and the generated training label information;
• and
• performing, by the terminal device based on the first sample data and the second sample data, model training on a graphical model sent by a server;
• acquiring, by the terminal device, gradient information corresponding to the trained graphical model;
• and
• sending, by the terminal device, the gradient information to the server for the server to update model parameters in the graphical model based on gradient information provided by different terminal devices to obtain an updated graphical model.

A terminal device comprising:

• one or more processors;
• and
• one or more memories coupled to the one or more processors and storing programming instructions for execution by the one or more processors for execution by the one or more processors to perform operations comprising:
• acquiring node information of a first graph to be constructed, node information of a second graph and node connection information of the second graph;
• constructing the first graph by using a predetermined fully connected network based on the node information of the first graph;
• constructing the second graph by using a predetermined graph network based on the node information and the node connection information of the second graph, wherein the predetermined fully connected network and the predetermined graph network have same network parameters;
• acquiring a latent vector of a first node that has training label information in the first graph;
• constructing first sample data by using a predetermined InstaHide privacy protection rule based on the latent vector of the first node and the training label information corresponding to the first node;
• generating training label information for a second node without training label information in the first graph and a node in the second graph;
• acquiring latent vectors of the second node and the node in the second graph;
• constructing second sample data by using the predetermined InstaHide privacy protection rule based on the latent vectors of the second node, the node in the second graph, and the generated training label information;
• and
• performing, based on the first sample data and the second sample data, model training on a graphical model sent by a server;
• acquiring gradient information corresponding to the trained graphical model;
• and
• sending the gradient information to the server for the server to update model parameters in the graphical model based on gradient information provided by different terminal devices to obtain an updated graphical model.