Novel stochastic algorithms for privacy-preserving utility mining

Abstract

High-utility itemset mining (HUIM) is a technique for extracting valuable insights from data. When dealing with sensitive information, HUIM can raise privacy concerns. As a result, privacy-preserving utility mining (PPUM) has become an important research direction. PPUM involves transforming quantitative transactional databases into sanitized versions that protect sensitive data while retaining useful patterns. Researchers have previously employed stochastic optimization methods to conceal sensitive patterns in databases through the addition or deletion of transactions. However, these approaches alter the database structure. To address this issue, this paper introduces a novel approach for hiding data with stochastic optimization without changing the database structure. We design a flexible objective function to let users restrict the negative effects of PPUM according to their specific requirements. We also develop a general strategy for establishing constraint matrices. In addition, we present a stochastic algorithm that applies the ant lion optimizer along with a hybrid algorithm, which combines both exact and stochastic optimization methods, to resolve the hiding problem. The results of extensive experiments are presented, demonstrating the efficiency and flexibility of the proposed algorithms.