Today's medical research is seen to be highly dependent on data exchange; unfortunately, despite its benefits, it frequently encounters problems, particularly issues with data privacy. As a result, several methods and infrastructures have been created to ensure that patients and research participants maintain their anonymity when data is exchanged. However, privacy protection often has a cost, such as limitations on the types of studies that may be done on shared data. The lack of a systematization that would make the trade-offs made by various techniques obvious is what needs to be addressed. In this research, develop the Feline-Storm Based Privacy Preservation Technique for multi-institutional clinical data. Data mining provides many advantages in various domains, particularly in medicine. The data about the disease is ensured to the experts, who can determine the effects, availability, and nature. The private information of the persons should not be disclosed to the expert groups, which ensures the confidentiality of the confidential information. Hence, to ensure the privacy of the people's electronic health records (EHR), this research utilizes the C-mixture and three privacy restraints that strengthen the privacy measures. Furthermore, the Hybrid Feline-storm algorithm, which emphasizes exploitation or the exploration phase at any instance, avoiding the local optima and the premature convergence to ensure the optimized privacy preserved of the data. This research also establishes security strategies such as K-anonymity, T-closeness, and L-diversity to attain complete data privacy. Further, the Feline-storm optimization is developed to minimize information loss. The information loss, class average size, and fitness measure achieved by the proposed methodology are 0.85, 0.38, and 4.7457, respectively.
| Published in | American Journal of Bioscience and Bioengineering (Volume 11, Issue 5) |
| DOI | 10.11648/j.bio.20231105.14 |
| Page(s) | 79-91 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Hybrid Feline-Storm Algorithm, Data Anonymization, C-mixture, Clinical Trial and Pharma Industry
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APA Style
Sagarkumar Patel, Rachna Patel. (2023). Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. American Journal of Bioscience and Bioengineering, 11(5), 79-91. https://doi.org/10.11648/j.bio.20231105.14
ACS Style
Sagarkumar Patel; Rachna Patel. Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. Am. J. BioSci. Bioeng. 2023, 11(5), 79-91. doi: 10.11648/j.bio.20231105.14
AMA Style
Sagarkumar Patel, Rachna Patel. Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm. Am J BioSci Bioeng. 2023;11(5):79-91. doi: 10.11648/j.bio.20231105.14
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Download@article{10.11648/j.bio.20231105.14,
author = {Sagarkumar Patel and Rachna Patel},
title = {Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm},
journal = {American Journal of Bioscience and Bioengineering},
volume = {11},
number = {5},
pages = {79-91},
doi = {10.11648/j.bio.20231105.14},
url = {https://doi.org/10.11648/j.bio.20231105.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20231105.14},
abstract = {Today's medical research is seen to be highly dependent on data exchange; unfortunately, despite its benefits, it frequently encounters problems, particularly issues with data privacy. As a result, several methods and infrastructures have been created to ensure that patients and research participants maintain their anonymity when data is exchanged. However, privacy protection often has a cost, such as limitations on the types of studies that may be done on shared data. The lack of a systematization that would make the trade-offs made by various techniques obvious is what needs to be addressed. In this research, develop the Feline-Storm Based Privacy Preservation Technique for multi-institutional clinical data. Data mining provides many advantages in various domains, particularly in medicine. The data about the disease is ensured to the experts, who can determine the effects, availability, and nature. The private information of the persons should not be disclosed to the expert groups, which ensures the confidentiality of the confidential information. Hence, to ensure the privacy of the people's electronic health records (EHR), this research utilizes the C-mixture and three privacy restraints that strengthen the privacy measures. Furthermore, the Hybrid Feline-storm algorithm, which emphasizes exploitation or the exploration phase at any instance, avoiding the local optima and the premature convergence to ensure the optimized privacy preserved of the data. This research also establishes security strategies such as K-anonymity, T-closeness, and L-diversity to attain complete data privacy. Further, the Feline-storm optimization is developed to minimize information loss. The information loss, class average size, and fitness measure achieved by the proposed methodology are 0.85, 0.38, and 4.7457, respectively.},
year = {2023}
}
TY - JOUR T1 - Enhanced Privacy Preservation Technique for the Multi Institutional Clinical Data Using Hybrid Feline-Storm Algorithm AU - Sagarkumar Patel AU - Rachna Patel Y1 - 2023/10/09 PY - 2023 N1 - https://doi.org/10.11648/j.bio.20231105.14 DO - 10.11648/j.bio.20231105.14 T2 - American Journal of Bioscience and Bioengineering JF - American Journal of Bioscience and Bioengineering JO - American Journal of Bioscience and Bioengineering SP - 79 EP - 91 PB - Science Publishing Group SN - 2328-5893 UR - https://doi.org/10.11648/j.bio.20231105.14 AB - Today's medical research is seen to be highly dependent on data exchange; unfortunately, despite its benefits, it frequently encounters problems, particularly issues with data privacy. As a result, several methods and infrastructures have been created to ensure that patients and research participants maintain their anonymity when data is exchanged. However, privacy protection often has a cost, such as limitations on the types of studies that may be done on shared data. The lack of a systematization that would make the trade-offs made by various techniques obvious is what needs to be addressed. In this research, develop the Feline-Storm Based Privacy Preservation Technique for multi-institutional clinical data. Data mining provides many advantages in various domains, particularly in medicine. The data about the disease is ensured to the experts, who can determine the effects, availability, and nature. The private information of the persons should not be disclosed to the expert groups, which ensures the confidentiality of the confidential information. Hence, to ensure the privacy of the people's electronic health records (EHR), this research utilizes the C-mixture and three privacy restraints that strengthen the privacy measures. Furthermore, the Hybrid Feline-storm algorithm, which emphasizes exploitation or the exploration phase at any instance, avoiding the local optima and the premature convergence to ensure the optimized privacy preserved of the data. This research also establishes security strategies such as K-anonymity, T-closeness, and L-diversity to attain complete data privacy. Further, the Feline-storm optimization is developed to minimize information loss. The information loss, class average size, and fitness measure achieved by the proposed methodology are 0.85, 0.38, and 4.7457, respectively. VL - 11 IS - 5 ER -
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