Simulation of Differential Clustering Method for Privacy Data of Different Dimensions Based on K-means
When clustering privacy data from different dimensions,if data privacy cannot be protected in a timely manner during the clustering process,it is easy to encounter data privacy leakage problems.To effectively enhance privacy and security during data clustering,a k-means based differential clustering method for privacy data in different dimensions was proposed.Firstly,the noise in data was removed.And then,CS reconstruction algorithm was used to construct the original signal of damaged data,thus repairing the data damage.To address the issue of data leakage when selecting the cluster center of k-means clustering algorithm,the differential k-means clustering algo-rithm was adopted for optimization.According to Laplacian mechanism,regularized artificial noise was added to the initial cluster centers to enhance the effect of data protection.Finally,we achieved the effective data clustering.The experimental results show that the proposed method has good clustering effect and high performance in private data clustering.
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