Open Access

ARTICLE

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

Hasanien K. Kuba¹, Mustafa A. Azzawi², Saad M. Darwish^3,*, Oday A. Hassen⁴, Ansam A. Abdulhussein⁵

1 University of Information Technology and Communication (UOITC), Baghdad, 10081, Iraq
2 Accenture, Ling Karan TRX, Kuala Lumpur, 55188, Malaysia
3 Department of Information Technology, Institute of Graduate Studies and Research, University of Alexandria, 21526, Egypt
4 Ministry of Education, Wasit Education Directorate, Kut, 52001, Iraq
5 Information Technology Center, Iraqi Commission for Computers and Informatics, Baghdad, 10081, Iraq

* Corresponding Author: Saad M. Darwish. Email:

Computers, Materials & Continua 2023, 74(2), 4119-4133. https://doi.org/10.32604/cmc.2023.033182

Received 10 June 2022; Accepted 02 September 2022; Issue published 31 October 2022

Abstract

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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Keywords

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