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

Abstract

The embodiment of the invention provides a method and device for performing multi-party joint dimension reduction processing on private data. The method comprises the steps: enabling each data holderin multiple parties to perform transposed multiplication operation on a locally owned private data matrix to obtain a product matrix; employing a public key of a third party to perform homomorphic encryption on the product matrix, and then summarizing the product matrix into a certain operation platform to perform homomorphic addition operation, and sending a homomorphic addition result to the third party; enabling the third party to decrypt the homomorphic addition result to obtain a covariance matrix required by principal component analysis so as to determine a dimensionality reduction transformation matrix, and broadcasting the dimensionality reduction transformation matrix to each holder. Therefore, each holder can use the dimension reduction transformation matrix to carry out dimension reduction processing. In this way, the security of private data in each holder is ensured.

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 for 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 dimensions ...

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, but 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

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