As a social information product, the privacy and usability of high-dimensional data are the core issues in the field of privacy protection. Feature selection is a commonly used dimensionality reduction processing technique for high-dimensional data. Some feature selection methods only process some of the features selected by the algorithm and do not take into account the information associated with the selected features, resulting in the usability of the final experimental results not being high. This paper proposes a hybrid method based on feature selection and a cluster analysis to solve the data utility and privacy problems of high-dimensional data in the actual publishing process. The proposed method is divided into three stages: (1) screening features; (2) analyzing the clustering of features; and (3) adaptive noise. This paper uses the Wisconsin Breast Cancer Diagnostic (WDBC) database from UCI’s Machine Learning Library. Using classification accuracy to evaluate the performance of the proposed method, the experiments show that the original data are processed by the algorithm in this paper while protecting the sensitive data information while retaining the contribution of the data to the diagnostic results.

In order to solve the problem of privacy disclosure when publishing high-dimensional data and to protect the privacy of frequent itemsets in association rules, a high-dimensional data publishing method based on frequent itemsets of association rules (PDP Growth) is proposed. This method, in a distributed framework, utilizes rough set theory to improve the mining of association rules. It optimizes association analysis while reducing the dimensionality of high-dimensional data, eliminating more redundant attributes, and obtaining more concise frequent itemsets, and uses the exponential mechanism to protect the differential privacy of the simplest frequent itemset obtained, and effectively protects the privacy of the frequent itemset by adding Laplace noise to its support. The theory validates that the method satisfies the requirement of differential privacy protection. Experiments on multiple datasets show that this method can improve the efficiency of high-dimensional data mining and meet the privacy protection. Finally, the association analysis results that meet the requirements are published.