Sharing healthcare information based on privacy preservation

Rashid, Asmaa Hatem; Yasin, Norizan Binti Mohd

doi:10.5897/sre11.862

Cited by 4 publications

References 72 publications

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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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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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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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The Collaborative Use of Patients' Health-Related Information

Alhaddadin

Gutiérrez

Liu

2018

Advances in Human Resources Management and Organizational Development

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The advancement in the field of information and communication technology has generated a great deal of information that was not possible to access earlier. Healthcare systems are one of the most beneficial applications using wireless medical sensor technologies, which can facilitate patient care within homes, hospitals, clinics, disaster sites, and the open environment. The integration and sharing of such information can contribute significantly to a better understanding of patients' health conditions and therefore to improving the quality of healthcare provided to them. However, in order to achieve sufficient levels of collaborative use of information among healthcare-related practitioners, there is a need to overcome a number of issues such as privacy and interoperability, among others. This chapter discusses two of the main challenges encountered before healthcare information systems can collaboratively share patients' records, namely privacy and interoperability.

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Sharing healthcare information based on privacy preservation

Cited by 4 publications

References 72 publications

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

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

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

The Collaborative Use of Patients' Health-Related Information

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