Advance privacy preserving in association rule mining

Chaudhari, M. B.; Varmora, Jigar

doi:10.1109/iceeot.2016.7755148

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…However, a high level of privacy is vital to protecting sensitive information when it is shared by data owners for the association rule mining process. Privacy preservation is a data mining technique that addresses the security of the knowledge extracted through data mining methods [7]. Recently, researchers have shed light on the challenges of Privacy Preserving Association Rule Mining (PPARM) because of the dilemma of choosing between disclosing sensitive association rules to generate highly accurate results and maintaining the privacy of these sensitive rules.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Protocols for the GT problem can be used to determine graphics similarity [17]. SMC protocols of the GT problem were used to solve secure computation of skyline query in mapreduce [18], secure stable matching at scale [19], private large-scale databases [20], association rule mining [21], and so forth. In addition, Bogdanov et al [22] proposed a tool for cryptographically secure statistical analysis based on a sorting method.…”

Section: Introductionmentioning

confidence: 99%

Efficient Solutions to Two-Party and Multiparty Millionaires’ Problem

Liu

Chen

et al. 2017

Security and Communication Networks

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The millionaires' problem is the basis of secure multiparty computation and has many applications. Using a vectorization method and the Paillier encryption scheme, we first propose a secure two-party solution to the millionaires' problem, which can determine = , < , or > in one execution. Subsequently, using the vectorization and secret splitting methods, we propose an information-theoretically secure protocol to solve the multiparty millionaires' problem (a.k.a. secure sorting problem), and this protocol can resist collusion attacks. We analyze the accuracy and security of our protocols in the semihonest model and compare the computational and communication complexities between the proposed protocols and the existing ones.

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