Privacy calculation method and system based on feature engineering IV value and readable storage medium

Abstract

The invention provides a privacy calculation method and system based on a feature engineering IV value and a readable storage medium, and the method comprises the steps: a participant A generates a public and private key pair of the participant A, and publishes a public key to a participant B; the participant A encrypts the label value of each piece of sample data by using a public key, and sends the ciphertext label value to the participant B; the participant B groups the plurality of sample data based on the characteristic values, and calculates a ciphertext IV value of each group in combination with the ciphertext label value of each sample data and the public key of the participant A; the participant B accumulates the ciphertext IV values of the groups to obtain a final ciphertext IV value; the participant B scrambles the final ciphertext IV value and then sends the final ciphertext IV value to the participant A; the participant A decrypts the final ciphertext IV scrambling value by using a private key to obtain a plaintext IV scrambling value and sends the plaintext IV scrambling value to the participant B; and the participant B performs descrambling on the plaintext IV scrambling value to obtain the plaintext IV value of the feature. According to the method, the privacy calculation of the multi-party feature engineering IV value can be realized.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a privacy calculation method, system and readable storage medium based on feature engineering IV values. Background technique [0002] In traditional machine learning algorithms, when the number of feature inputs is large, it is necessary to calculate the feature engineering IV value (abbreviated as IV value) for screening. The higher the IV value, the greater the amount of information contained in the feature, and the more suitable for model training of machine learning algorithms . With the increasing importance of data privacy and security protection, the method of calculating IV value based on plaintext data has been difficult to meet the requirements of privacy protection. Therefore, the technical method of IV value calculation based on privacy protection technology has received more and more attention. . Traditionally, the following two privacy pro...

AI Technical Summary

Problems solved by technology

However, the privacy computing program based on TEE technology needs to run on a CPU that supports TEE technology, so it needs to have absolute trust in the CPU manufacturer, which also makes the security model of TEE technology need to rely on the CPU manufacturer

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