KNN classification service system and method supporting privacy protection

Abstract

The invention belongs to the field of machine learning and privacy protection, and in particular relates to a KNN classification service system and method supporting privacy protection. The architecture of the system includes: the model owner and the client; the method of the KNN classification service system that supports privacy protection, including: 1) the preparation stage, generating public and private keys, and encrypting the training data according to the public key; 2) the classification stage, the interaction between the two parties Key; the client encrypts the data to be tested with the public key, and the model owner uses a security protocol based on the encrypted training data to cooperate with the client to complete the encrypted data classification, and finally obtains the classification result and sends it to the client. The present invention uses homomorphic encryption to encrypt the training data and the data to be tested, and constructs a safe basic protocol by combining secure multi-party computing technology with homomorphic encryption. Realize the analysis and prediction of personal data without revealing privacy.

Description

technical field [0001] The invention belongs to the field of machine learning and privacy protection, and in particular relates to a KNN classification service system and method supporting privacy protection. Background technique [0002] The KNN classification service, that is, the k-Nearest Neighbor (KNN) classifier may leak user privacy information during the sample training and classification stages. In the sample training stage, the data owner does not want the data information he owns to be leaked out, and even keep the trainer confidential, which requires encryption of the training data. In the classification stage, the trainer will use the obtained model W as a component of the classifier, and publish the classifier to provide services, but does not want the results to be obtained by a third party, which requires encryption of the classification model and test vector. Therefore, for classifiers, whether it is the training phase or the classification phase, the issue...

AI Technical Summary

Problems solved by technology

[0006] Although the combination of homomorphic encryption scheme and secure multi-party computation can partially solve the problem of classifier data privacy protection, and there have been some research results on classifier privacy protection, but there are still the following problems: 1) Most of the schemes are aimed at training There is little protection for the classification model and classification process for the privacy protection of stage data; 2) Its security settings are low, and it is easy to leak the classification model and classification results; 3) Homomorphic encryption operations support polynomial operations of addition and multiplication , the comparison operation can also be obtained through secure multi-party computing, but the machine learning calculation overhead is large and the efficiency is low

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