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

A privacy data and dimensionality reduction technology, applied in the fields of electronic digital data processing, digital data protection, computer security devices, etc., can solve the problems of redundancy, limitation, and inability to handle machine learning models, and achieve the effect of ensuring security without leakage

Active Publication Date: 2022-05-17
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
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  • Description
  • Claims
  • Application Information

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

Method used

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

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Embodiment Construction

[0063] The solutions provided in this specification will be described below in conjunction with the accompanying drawings.

[0064] figure 1 It is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification. Such as figure 1 As shown, in the shared learning scenario, the data set is jointly provided by multiple holders 1, 2, ..., M (M is a natural number), and each holder owns a part of the data in the data set. The data set may be a training data set for training the neural network model, a test data set for testing the neural network model, or a data set to be predicted. The data set can include attribute characteristic data of business objects, and the business objects can be objects to be analyzed in various businesses such as users, merchants, commodities, and events.

[0065] There can be at least two data distributions here. One is that each holder owns the data of the same attribute item of different business objects. For exa...

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Abstract

The embodiment of this specification provides a multi-party joint dimension reduction method and device for private data. In the case of vertical distribution of private data, the first holder performs zero-meanization on the first original matrix to obtain the first center matrix. An N*N-dimensional asymmetric orthogonal matrix is ​​obtained, the asymmetric orthogonal matrix is ​​multiplied by the first center matrix to obtain a first secret matrix, and the first secret matrix is ​​sent to a trusted third party. A trusted third party concatenates each hidden matrix to get the global hidden matrix, multiplies the global hidden matrix with its transposed matrix to get the covariance matrix, solves the eigenvalues ​​of the covariance matrix to get the dimensionality reduction transformation matrix, and calculates the dimensionality reduction transformation matrix After splitting, each split matrix is ​​obtained and sent to the holder. The first holder uses the first splitting matrix to process the first original matrix to obtain the first dimensionality reduction matrix, which is used to perform business forecast analysis on the business object by means of machine learning.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06F21/62G06N20/00
CPCG06F21/6245G06N20/00G06F18/2135
Inventor 刘颖婷陈超超王力周俊
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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