A Mean Estimation Method and Device Based on Classification Transform Disturbance Mechanism

Abstract

A mean value estimation method and device based on a classification transformation perturbation mechanism, belonging to the technical field of information security. Two-dimensional binary classification data; use the random response mechanism to perturb the transformed data, and then randomly and uniformly extract the values ​​from the numerical segment identified by the perturbed data as the perturbation value; compared with other methods, this method can satisfy the local differential privacy. At the same time, higher data utility can be obtained in data analysis tasks such as mean estimation, and the obtained model has higher classification accuracy and better performance.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to a mean value estimation method and device based on a classification transformation perturbation mechanism. Background technique [0002] As a branch of differential privacy, the local differential privacy mechanism provides a stronger privacy guarantee than differential privacy. The most typical perturbation mechanism is the random response mechanism. In local differential privacy, it is assumed that the server is not trusted, and the user does not directly send the original data to the server, but perturbs the data locally to satisfy the local differential privacy, and then sends the perturbed data to the server. The server performs corresponding data analysis tasks on the collected noise data to obtain the required statistical information. Using local differential privacy for privacy protection does not require a large number of complex operations, and...

AI Technical Summary

Problems solved by technology

However, the accuracy of existing local differential privacy mechanisms is poor, and there is still room for improvement

And most of these methods perturb the data directly, ignoring the possibility of converting the data type for perturbation, ignoring the possibility of perturbing by changing the data type to balance the utility and privacy of the data

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