High Dimensional Data Differential Privacy Protection Publishing Method Based on Association Analysis

Abstract

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.