Method and device for multi-party joint dimension reduction processing of private data

Abstract

The embodiment of the invention provides a method and device for carrying out dimension reduction processing on private data in a multi-party joint mode. The method comprises the steps: under the condition that the private data is longitudinally distributed, a first holding party performs zero equalization on a first original matrix to obtain a first center matrix, obtains an N*N asymmetric orthogonal matrix, multiplies the asymmetric orthogonal matrix by the first center matrix to obtain a first secret matrix, and sends the first secret matrix to a trusted third party; the trusted third partysplices the secret matrixes to obtain a global secret matrix, multiplies the global secret matrix by the transposed matrix thereof to obtain a covariance matrix, performs eigenvalue solving on the covariance matrix to obtain a dimension reduction transformation matrix, splits the dimension reduction transformation matrix to obtain split matrixes, and sends the split matrixes to a holder; the first holder processes the first original matrix by using the first split matrix to obtain a first dimension reduction matrix, wherein the first dimension reduction matrix is used for performing businessprediction analysis on the business object in a machine learning mode.

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular to a method and device for multi-party joint dimensionality reduction processing on private data. Background technique [0002] The data needed for machine learning often involves multiple platforms and 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 is facing great resistance. How to integrate data scattered across various platforms under the premise of ensuring that data is not leaked has become a challenge. [0003] On the other hand, as the amount of data increases, the d...

AI Technical Summary

Problems solved by technology

Although a large amount of high-dimensional data can enrich the training sample data of machine learning, in fact, these high-dimensional data often have some redundant information

The help of redundant information to the effect of machine learning is very limited, and the resulting high-dimensional feature data may cause "dimension explosion", making it difficult for machine learning models to handle and affecting the training efficiency of the model

Images