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

Wu, Jimmy Ming‐Tai; Srivastava, Gautam; Yun, Unil; Tayeb, Shahab; Lin, Jerry Chun‐Wei

doi:10.1002/ett.4209

Cited by 13 publications

(8 citation statements)

References 32 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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“…Recently, the Internet of Things (IoT) systems have been excessively deployed in our daily life activities. [1][2][3][4] This, as a result, introduces several practical challenges to deal with the massive number of connected devices, including the limited licensed bandwidth resources. 5 In specific, the available licensed bandwidth cannot support the expected massive connectivity, especially when considering the IoT sensors systems.…”

Section: Introductionmentioning

confidence: 99%

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

Al‐Obiedollah

Arabiat

Al-Ajlouni

et al. 2022

Trans Emerging Tel Tech

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The recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

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

Cited by 13 publications

References 32 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

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

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