MOSAR: A Multi-Objective Strategy for Hiding Sensitive Association Rules Using Genetic Algorithm

Motlagh, Farzad Nourmohammadzadeh; Sajedi, Hedieh

doi:10.1080/08839514.2016.1268038

Cited by 10 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study [29] proposed another formula for the GA fitness function to apply the hiding process to sensitive association rules using recursion. The following three measures were defined: Availability, Sensitivity, and Conflict.…”

Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

“…The results of the performance studies [27]- [29] showed that the privacy of sensitive association rules should be further improved, which can be achieved by hybridizing optimization algorithms. Hence, in 2022, Navale and Mali [30] developed an integration approach called the Genetic Algorithm with Crow Search Algorithm (GA-CSA) to address not only PPARM data sanitization but also the restoration process.…”

Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

See 1 more Smart Citation

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

Aljehani,

Alotaibi

2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

Section: ) Evolutionary Algorithms (Eas)mentioning

confidence: 99%

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

Aljehani,

Alotaibi

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Furthermore, in recent years, many works were presented to solve the privacy-preserving association rules mining problem. For instance, Motlagh and Sajedi [9] designed an MOEAbased algorithm MOSAR, but the computational costs of MOSAR are too high since it hides one SAR at one time. Besides, Cheng [33] investigated the frequent itemsets hiding problem and presented a novel MOEAbased method.…”

Section: Evolutionary Algorithm-based Miningmentioning

confidence: 99%

“…First, they are low data privacy. To solve the SARP problem, some researchers protect the sensitive information by hiding the SARs, which is achieved by decreasing the frequency [9] . However, it is limited in terms of practicality because only one SAR can be protected at one time.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Sanitization Approach for Minable Data Publication

Yang

Liao

2022

Big Data Min. Anal.

View full text Add to dashboard Cite

Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle.Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.

show abstract

Hiding sensitive itemsets with multiple objective optimization

Lin

Zhang

et al. 2019

Soft Comput

View full text Add to dashboard Cite

MOSAR: A Multi-Objective Strategy for Hiding Sensitive Association Rules Using Genetic Algorithm

Cited by 10 publications

References 19 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

An Optimized Sanitization Approach for Minable Data Publication

Hiding sensitive itemsets with multiple objective optimization

Contact Info

Product

Resources

About