Sample classification method of distributed privacy protection logistic regression model based on hybrid protocol

Abstract

The invention discloses a sample classification method of a distributed privacy protection logistic regression model based on a hybrid protocol. The method is applied to a distributed logistic regression model training scene composed of n data providers, an encryption service provider and a data aggregator, and comprises the following steps: S1, an initialization stage; S2, a distributed model training stage; S3, a final model release stage. According to the method, the problem of privacy leakage in the process that a plurality of data providers jointly use logistic regression models to perform sample classification at present can be solved, so a sample classification task can be completed under the condition that private data is not leaked, and the safety of the sample classification process and the utilization rate of sensitive data are improved.

Description

technical field [0001] The invention belongs to the field of network and information security, and specifically relates to a sample classification method of a distributed privacy protection logic regression model based on a hybrid protocol. Background technique [0002] In recent years, machine learning techniques have been increasingly used in practice to generate prediction or classification models to solve specific prediction and classification tasks. With the development of big data, a large amount of data has been generated. These data provide a larger-scale training data set for the training process of the prediction model or classification model to obtain better prediction or classification results. However, these data are often held by different sites. Due to the restrictions of laws and regulations or their own interests, these sites hope to jointly carry out data mining tasks without disclosing their own private data. For example, multiple institutions each have ...

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Problems solved by technology

[0004] The present invention is to solve the deficiencies in the above-mentioned prior art, and proposes a classification method based on a hybrid protocol-based distributed privacy protection logistic regression model, in order to be able to Solve the privacy protection problem in the current sample classification process, improve the security of users' distributed joint classification tasks, and use ciphertext packaging technology to effectively reduce the overall calculation and communication overhead, so that it can be used without leaking private information. Get better classification results and increase utilization of sensitive data

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