Safe feature engineering method and device

Abstract

The invention provides a safe feature engineering method and device. The first device may transmit a first data set to the second device, the first data set including a ciphertext of tag information of the plurality of data objects. The second device may perform feature engineering processing on a second data set including feature data of the plurality of data objects to generate a subset of the second data set. The second device may generate a ciphertext subset of the tag information of the corresponding data object in the first data set according to the subset of the second data set, and send the ciphertext of the ciphertext subset to the first device. The first device can decrypt the ciphertext subset and generate statistical information, and send the statistical information to the second device. The second device may use the statistical information to calculate a characteristic engineering index. The invention further provides a corresponding feature engineering device.

Description

technical field [0001] The present disclosure relates to feature engineering, and in particular to a safe feature engineering method and device. Background technique [0002] The modeling process requires basic feature engineering processing on sample data, and then model training with the data set processed by feature engineering. Feature engineering refers to the process of converting raw data into feature vectors. Feature engineering is the most important initial step in machine learning, which will directly affect the effect of machine learning, and usually requires a lot of time. Typical feature engineering includes processes such as data cleaning, feature discretization, feature extraction, and feature selection. In big data scenarios, data is usually stored in a distributed manner, and feature engineering processing requires the fusion of modeling sample data from all parties. For example, when binning data, it needs the cooperation of feature data and label inform...

AI Technical Summary

Problems solved by technology

The disadvantage of this solution is that the data plaintext still needs to be output externally, which cannot completely eliminate the leakage of private data, nor can it solve the problem of data trust itself

In addition, it is hard to find a completely trusted third party, and there are hidden dangers in data transmission security during the transmission of data / models to trusted third parties

Not only that, there is an operation authority problem in the trusted third party, it is difficult to control the granularity of security checks on data access, and the results of feature engineering processing may also be leaked

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