An Optimized Sanitization Approach for Minable Data Publication

Yang, Fan; Liao, Xiaofeng

doi:10.26599/bdma.2022.9020007

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…In the context of PPARM, Yang and Liao [46] presented a new optimized sanitization approach for minable data publication (SA-MDP) to address the PPARM problem. Because the evolution method of any metaheuristic-based algorithm significantly affects the search space, the original PSO algorithm was modified by adopting two mechanisms to update the location of the particles.…”

Section: A: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

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The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workaround for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, providing a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups with discussion of the nature of the representation schemes for each category identified in the survey. This review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions are presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities of metaheuristic-based algorithms in the context of PPARM.

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Section: A: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Aljehani,

Alotaibi

2024

IEEE Access

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“…Based on the optimization algorithm of machine learning model, through the processing of the effective information stored in the existing data, the relevant formulas and algorithms are used to realize big data mining [3][4]. This method can effectively solve the problem of low accuracy due to collinearity and random errors in traditional large sample problems [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization Algorithm of Big Data Mining Based on Machine Learning Model

2023

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With the arrival of the big data era, massive data storage, massive information push, and massive complex products and services are flooding the whole society. As an important and effective method, machine learning model has been widely used in various fields. In order to apply data mining technology to practice well, this paper makes an in-depth study on machine learning model and data mining optimization methods. This paper mainly uses the methods of experiment and comparison, and puts forward the advantages of various improved algorithms through the detection of fragments, orchids and beverages. The experimental results show that the C index obtained by ESPSO-FCM on fragments is 0.523, which is larger than the other two algorithms. ESPSO-FCM algorithm is an improved clustering algorithm with higher convergence accuracy, stronger partition ability and better clustering quality.

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Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

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An Optimized Sanitization Approach for Minable Data Publication

Cited by 5 publications

References 30 publications

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Preserving Privacy in Association Rule Mining Using Metaheuristic-Based Algorithms: A Systematic Literature Review

Optimization Algorithm of Big Data Mining Based on Machine Learning Model

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

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