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

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