Hiding sensitive itemsets with multiple objective optimization

Lin, Jerry Chun‐Wei; Zhang, Yuyu; Zhang, Binbin; Fournier-Viger, Philippe; Djenouri, Youcef

doi:10.1007/s00500-019-03829-3

Cited by 47 publications

(24 citation statements)

References 34 publications

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“…Several experiments were carried out to compare the effectiveness and efficiency of the proposed GMPSO algorithm to the state-of-the-art single-objective cpGA2DT [19] and NSGA-II-based [23] approaches. The algorithms used in the experiments were implemented in Java, using a PC with an Intel Core i7-6700 quad-core processor and 8 GB of RAM under the 64-bit Microsoft Windows 10 operating system.…”

Section: Resultsmentioning

confidence: 99%

“…This approach is based on a multi-objective method, although incomplete transactions can therefore be produced; a misleading decision can thus occur, particularly in the treatment of hospital diagnoses. Lin et al presented a meat-heuristic approach [23] based on the NSGA-II framework for data sanitization, which shows better side effects compared to single-objective algorithms, which is the state-of-the-art multiple-objective optimization algorithm in PPDM. Several works of privacy-preserving and security issues are extensively studied in different applications.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

“…In this section, the state-of-the-art single-objective cpGA2DT [19] and multi-objective NSGA-II-based model [23] are then compared to the designed GMPSO in terms of the hiding failure (α), missing cost (β), artificial cost (γ) and database dissimilarity (Dis). The number of populations for all evolutionary algorithms is set to 50.…”

Section: Side Effectsmentioning

confidence: 99%

“…Non-dominated Sorting Genetic Algorithm (NSGA)-II [22] is a way to regard multi-objective functions rather than a single-objective function. Lin et al [23] developed an algorithm by adapting the NSGA-II model for data sanitization in PPDM. The multi-objective particle swarm optimization (MOPSO) algorithm [24] was extended from a conventional particle swarm optimization (PSO) algorithm [25], but handles multi-objective problems to find a set of Pareto solutions.…”

Section: Introductionmentioning

confidence: 99%

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A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

2019

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Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

Section: Side Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

2019

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“…us, the side effects are effectively reduced. Lin et al [27] presented a multiobjective algorithm (NSGA2DT) for hiding sensitive itemsets with transaction deletion. A Fast SoRting strategy and the prelarge concept are utilized to accelerate the iterative process.…”

Section: Related Workmentioning

confidence: 99%

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

Liu

Chen

Wen

et al. 2020

Mathematical Problems in Engineering

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High-utility pattern mining is an effective technique that extracts significant information from varied types of databases. However, the analysis of data with sensitive private information may cause privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, privacy-preserving utility mining (PPUM) has become an important research topic in recent years. The MSICF algorithm is a sanitization algorithm for PPUM. It selects the item based on the conflict count and identifies the victim transaction based on the concept of utility. Although MSICF is effective, the heuristic selection strategy can be improved to obtain a lower ratio of side effects. In our paper, we propose an improved sanitization approach named the Improved Maximum Sensitive Itemsets Conflict First Algorithm (IMSICF) to address this issue. It dynamically calculates conflict counts of sensitive items in the sanitization process. In addition, IMSICF chooses the transaction with the minimum number of nonsensitive itemsets and the maximum utility in a sensitive itemset for modification. Extensive experiments have been conducted on various datasets to evaluate the effectiveness of our proposed algorithm. The results show that IMSICF outperforms other state-of-the-art algorithms in terms of minimizing side effects on nonsensitive information. Moreover, the influence of correlation among itemsets on various sanitization algorithms’ performance is observed.

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An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint

Wu¹,

Srivastava

Yun

et al. 2021

Trans Emerging Tel Tech

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Privacy-preserving data mining (PPDM) is a popular research topic in the data mining field. For individual information protection, it is vital to protect sensitive information during data mining procedures. Furthermore, it is also a serious offense to spill sensitive private knowledge. Recently, many PPDM data mining algorithms have been proposed to conceal sensitive items in a given database to disclose high-frequency items. These recent methods have already proven to be excellent in protecting confidential information and maintaining the integrity of the input database. All prior techniques, however, ignored a crucial problem in setting minimum support thresholds. If a sensitive itemset includes more items, it will cause it the become more likely to be found. Before performing mining processes, a fixed value of the minimum support threshold will be set. In this paper, a new concept of minimal support for solving this issue is proposed. In compliance with a given threshold function, the proposed approach would set a tighter threshold for an object containing several items. The results of the experiments show the performance of the traditional Greedy PPDM approach, Genetic algorithm (GA)-based PPDM approaches, and the proposed particle swarm optimization-based algorithm with the new minimal support function. The results show that the proposed method performs similarly to conventional algorithms and offers higher protection than previous methods.

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Hiding sensitive itemsets with multiple objective optimization

Cited by 47 publications

References 34 publications

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

An Improved Sanitization Algorithm in Privacy-Preserving Utility Mining

An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint

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