Determining model parameters using secret sharing

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

• obtaining a first share of a Hessian matrix for a data processing model, wherein the Hessian matrix is secretly shared between the first data party device and a second data party device of a cooperation partner such that the first data party device and the second data party device have respective shares of the Hessian matrix, and wherein the Hessian matrix is based on feature data for the data processing model and an activation function for the data processing model;
• obtaining, using secret sharing with the second data party device, a first share of a product of a random number matrix and the Hessian matrix;
• determining, using secret sharing with the second data party device, a first share of a product of a first inverse matrix and a gradient of a loss function of the data processing model, wherein the first inverse matrix is an inverse of the Hessian matrix;
• and
• determining a first share of a new model parameter for the data processing model based on an original model parameter of the data processing model and the first share of the product of and the gradient of the loss function.

A computer-implemented system, comprising:

• one or more computers of a first data party device;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform operations comprising:
• obtaining a first share of a Hessian matrix for a data processing model, wherein the Hessian matrix is secretly shared between the first data party device and a second data party device of a cooperation partner such that the first data party device and the second data party device have respective shares of the Hessian matrix, and wherein the Hessian matrix is based on feature data for the data processing model and an activation function for the data processing model;
• obtaining, using secret sharing with the second data party device, a first share of a product of a random number matrix and the Hessian matrix;
• determining, using secret sharing with the second data party device, a first share of a product of a first inverse matrix and a gradient of a loss function of the data processing model, wherein the first inverse matrix is an inverse of the Hessian matrix;
• and
• determining a first share of a new model parameter for the data processing model based on an original model parameter of the data processing model and the first share of the product of the first inverse matrix and the gradient of the loss function.