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High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE umu -GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm namely HUIM-BPSO sig is proposed to efficiently find HUIs. It first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles of the designed HUIM-BPSO sig algorithm. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is an extension of frequent itemset mining, which discovers itemsets yielding a high profit in transaction databases (HUIs). In recent years, a major issue that has arisen is that data publicly published or shared by organizations may lead to privacy threats since sensitive or confidential information may be uncovered by data mining techniques. To address this issue, techniques for privacy-preserving data mining (PPDM) have been proposed. Recently, privacy-preserving utility mining (PPUM) has become an important topic in PPDM. PPUM is the process of hiding sensitive HUIs (SHUIs) appearing in a database, such that the resulting sanitized database will not reveal these itemsets. In the past, the HHUIF and MSICF algorithms were proposed to hide SHUIs, and are the state-of-the-art approaches for PPUM. In this paper, two novel algorithms, namely Maximum Sensitive Utility-MAximum item Utility (MSU-MAU) and Maximum Sensitive Utility-MInimum item Utility (MSU-MIU), are respectively proposed to minimize the side effects of the sanitization process for hiding SHUIs. The proposed algorithms are designed to efficiently delete SHUIs or decrease their utilities using the concepts of maximum and minimum utility. A projection mechanism is also adopted in the two designed algorithms to speed up the sanitization process. Besides, since the evaluation criteria proposed for PPDM are insufficient and inappropriate for evaluating the sanitization performed by PPUM algorithms, this paper introduces three similarity measures to respectively assess the database structure, database utility and item utility of a sanitized database. These criteria are proposed as a new evaluation standard for PPUM. [ABSTRACT FROM AUTHOR]

Privacy-preserving data mining (PPDM) has become an important research field in recent years, as approaches for PPDM can discover important information in databases, while ensuring that sensitive information is not revealed. Several algorithms have been proposed to hide sensitive information in databases. They apply addition and deletion operations to perturb an original database and hide the sensitive information. Finding an appropriate set of transactions/itemsets to be perturbed for hiding sensitive information while preserving other important information is a NP-hard problem. In the past, genetic algorithm (GA)-based approaches were developed to hide sensitive itemsets in an original database through transaction deletion. In this paper, a particle swarm optimization (PSO)-based algorithm called PSO2DT is developed to hide sensitive itemsets while minimizing the side effects of the sanitization process. Each particle in the designed PSO2DT algorithm represents a set of transactions to be deleted. Particles are evaluated using a fitness function that is designed to minimize the side effects of sanitization. The proposed algorithm can also determine the maximum number of transactions to be deleted for efficiently hiding sensitive itemsets, unlike the state-of-the-art GA-based approaches. Besides, an important strength of the proposed approach is that few parameters need to be set, and it can still find better solutions to the sanitization problem than GA-based approaches. Furthermore, the pre-large concept is also adopted in the designed algorithm to speed up the evolution process. Substantial experiments on both real-world and synthetic datasets show that the proposed PSO2DT algorithm performs better than the Greedy algorithm and GA-based algorithms in terms of runtime, fail to be hidden (F-T-H), not to be hidden (N-T-H), and database similarity (DS). [ABSTRACT FROM AUTHOR]