Privacy risk control method for multi-layer embedding differential privacy into decision tree model

Abstract

The invention provides a privacy risk control method for embedding differential privacy into a decision tree model in multiple layers, including initialization, embedding differential privacy technology into a multi-layer decision tree model, and obtaining a multi-layer decision tree. The present invention can embed the differential privacy technology into the decision tree model by adopting a multi-layer embedding method; compared with the prior art, it still satisfies the decision tree model under the protection of differential privacy, and the prediction accuracy of the model is higher than that of the prior art Greater improvement.

Description

technical field [0001] The invention relates to a privacy protection technology differential privacy and a data mining technology decision tree model, in particular to a privacy risk control method for embedding multi-layer differential privacy into a decision tree model. Background technique [0002] Differential privacy technology is a new privacy protection technology that can quantitatively control the risk of privacy leakage. Embedding differential privacy into the decision tree model can protect the decision tree model. In the previous research, the method of embedding differential privacy into the decision tree model is a layer of embedding. [0003] The realization of differential privacy requires the implementation of exponential mechanism and Laplacian mechanism, which contains uncertainty and noise, so the prediction accuracy of the decision tree model with differential privacy will be very low. It is lower than the tolerance of error in industrial systems, so t...

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Problems solved by technology

[0003] The realization of differential privacy requires the implementation of exponential mechanism and Laplacian mechanism, which contains uncertainty and noise, so the prediction accuracy of the decision tree model with differential privacy will be very low

It is lower than the tolerance of error in industrial systems, so the decision tree model with differential privacy protection has not been applied in industrial systems

[0004] Using this algorithm can realize a decision tree model with differential privacy technology protection, but the prediction accuracy of the decision tree model of this algorithm is low and cannot be applied to industrial systems

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