Method and device for checking model feature significance based on multi-party security calculation

A test value and feature matrix technology, applied in the field of machine learning, to achieve the effect of feature significance test

Active Publication Date: 2020-03-17
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to issues such as industry competition, data security, and user privacy, data integration faces great resistance. How to integrate data scattered across various platforms without data leakage has become a challenge

Method used

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  • Method and device for checking model feature significance based on multi-party security calculation
  • Method and device for checking model feature significance based on multi-party security calculation
  • Method and device for checking model feature significance based on multi-party security calculation

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Experimental program
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Embodiment Construction

[0042] Embodiments of this specification will be described below with reference to the accompanying drawings.

[0043] figure 1 A schematic diagram of an implementation scene according to an embodiment of this specification is shown. Such as figure 1 As shown, in the shared learning scenario, the training data is jointly provided by multiple holders (three data holders A, B, and C are schematically shown in the figure), and each holder owns a part of the training data. The training data includes, for example, N training samples, and each training sample includes a label value and respective feature values ​​of K features. The respective tag values ​​of the N samples can be represented by a vector y, which is held, for example, by party B as shown in the figure. The eigenvalues ​​of the K features of the N samples can be represented by an N×K feature matrix X, so that each data holder can hold a piece of data in the N×K matrix X, for example, the piece of data can be It is ...

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Abstract

The embodiment of the invention provides a method and device for checking the feature significance of a linear regression model based on multi-party security calculation. The method is executed by equipment of a first data holder in a plurality of data holders. N samples and model parameters of the model are jointly stored in respective devices of a plurality of data owners. The method comprises the steps of jointly executing matrix addition and matrix multiplication based on secret sharing with devices of other data owners to obtain an error quadratic sum of the N samples; jointly executing matrix addition and / or matrix multiplication based on secret sharing with other data holder equipment to obtain the value of the jth item on the diagonal line of the first matrix; calculating a secondnumerical value corresponding to the jth t test value; and executing matrix addition based on secret sharing in combination with equipment of other data owners to obtain the jth t test value, so as todetermine the significance of the corresponding characteristics of the linear regression model based on the jth t test value.

Description

technical field [0001] The embodiment of this specification relates to the field of machine learning technology, and more specifically, relates to a method and device for checking feature significance of a linear regression model based on multi-party secure calculation. 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] In a linear ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00G06F17/18G06F21/62
CPCG06F17/18G06N20/00G06F21/6245G06F18/214
Inventor 刘颖婷陈超超王力周俊
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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