A spectral clustering method based on differential privacy preservation

Abstract

The invention is applicable to the technical field of privacy protection, and provides a spectral clustering method based on differential privacy protection. The method includes the steps of pre-processing sample data; calculating a similarity matrix; based on a k-near-value, simplifying the similarity matrix; adding the random noise satisfying Laplace distribution to the similarity matrix; constructing an adjacent matrix and a degree matrix based on the similarity matrix after random noise perturbation; obtaining the Laplace matrix based on adjacency matrix and degree matrix; obtaining the first m large eigenvalues and corresponding eigenvectors of Laplace matrices; normalizing the eigenvector to form eigenmatrix; using k-means clustering method to cluster the feature matrix to get the label of clustering. A spectral clustering algorithm is used to calculate the sample similarity between the sample data as the weight value between the data points, and then differential privacy algorithm is used to add random noise of Laplace distribution to the weight value to interfere with the weight value to achieve the purpose of privacy preservation. The interfered data can not only achieve privacy preservation but also ensure the effectiveness of clustering.

Description

technical field [0001] The invention belongs to the technical field of privacy protection and provides a spectral clustering method based on differential privacy protection. Background technique [0002] In recent years, with the vigorous development of the Internet and information technology, the generation of massive data can provide researchers with many effective information resources. Mining and analyzing these massive data can obtain very valuable information, among which cluster analysis is an effective One of the means. However, there is also the risk of privacy leakage in the process of clustering. [0003] Nowadays, there are more and more applications of clustering analysis in privacy protection, and clustering, as one of the main technologies of data mining and machine learning, has been studied by scholars. Traditional clustering protection algorithms such as k-means, DBScan, k-medoids dynamic clustering, the traditional clustering algorithm has the problems o...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present invention provides a spectral clustering method based on differential privacy protection, aiming to solve the problems of privacy leakage and poor clustering effect in traditional clustering algorithms

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