Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules

Ranjith, K. S.; Amalanathan, Geetha Mary

doi:10.1007/978-981-15-0135-7_32

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…To preserve privacy and facilitate discovery, researchers have recently turned to a deep reinforcement learning approach for database sanitization [34][35][36]. The interested reader is directed to the recent studies in [37][38][39][40][41] for more reading in this area.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

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It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Kuba¹,

Azzawi²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Recently, a deep reinforcement learning technique has been applied to sanitize sensitive data from a database while still protecting privacy and allowing for knowledge discovery [33] [34]. For more information in this area, the reader can refer to the recent research in [35][36][37][38][39].…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Darwish

Essa²,

Osman³

et al. 2022

IEEE Access

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Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

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Survey on association rule analysis: Exploration using mining analysis

Gangaramani,

Londhe

2024

HIS

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Associative rule mining is a technique for discovering common patterns and correlations in data sets from different databases, including relational, transactional and other types of data repositories, such as relational databases. Different types of patterns exist in data mining such as frequent patterns, extended patterns, regular patterns etc. Many searches have focused on finding the frequent patterns and very little work has been carried out on negative or rare patterns. It has also been observed that only those items which are positively correlated(frequent) are been executed by various algorithms but very less attention is been given to negatively correlated items. Negatively correlated items also called infrequent items are the items which negate with each other. The items which do not satisfy the minimum threshold value generally are always been ignored by many researchers. Mining of Negative association helps in business such as for customer segmentation, in risk management as well as in medical field. So the main aim of writing this paper is to provide a short overview of various research issues involved in finding out positive and negative associations.

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Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules

Cited by 3 publications

References 11 publications

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Survey on association rule analysis: Exploration using mining analysis

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