Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms
With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protec...
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| Vydáno v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 8 |
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| Jazyk: | angličtina |
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Hindawi
24.03.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time. |
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| AbstractList | With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time. With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time.With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time. |
| Audience | Academic |
| Author | Khan, Shakir Deb, Nabamita Othman, Nashwan Adnan N, Gnanaprakasam C. Saravanan, V. Lakshmi, T. Jaya |
| AuthorAffiliation | 5 Department of Information Technology, Gauhati University, Gawahati, Assam 781014, India 1 College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia 3 Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai 600119, Tamilnadu, India 4 Department of Computer Science and Engineering, SRM University, Amaravati, AP, India 2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Oromia Region, Ethiopia 6 Department of Computer Science, College of Science, Knowledge University, Erbil 44001, Iraq |
| AuthorAffiliation_xml | – name: 5 Department of Information Technology, Gauhati University, Gawahati, Assam 781014, India – name: 1 College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia – name: 4 Department of Computer Science and Engineering, SRM University, Amaravati, AP, India – name: 3 Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai 600119, Tamilnadu, India – name: 2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Oromia Region, Ethiopia – name: 6 Department of Computer Science, College of Science, Knowledge University, Erbil 44001, Iraq |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35371203$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1109_TCE_2023_3323206 crossref_primary_10_1007_s11082_023_05277_8 crossref_primary_10_1007_s13198_023_02191_w crossref_primary_10_4015_S1016237224500601 crossref_primary_10_1007_s10115_025_02464_9 crossref_primary_10_3390_diagnostics13152610 crossref_primary_10_1109_TIFS_2025_3601539 crossref_primary_10_3389_fphy_2024_1410089 crossref_primary_10_1155_2023_9815652 crossref_primary_10_3390_su141912757 crossref_primary_10_2196_53008 |
| Cites_doi | 10.1109/SAINT.2012.11 10.1109/ACCESS.2019.2936301 10.1109/MILCOM.1997.648724 10.1109/ECACE.2019.8679506 10.1109/CHINACOM.2008.4685178 10.1109/RAICS.2011.6069408 10.1093/comjnl/bxu102 10.1109/FSKD.2013.6816364 10.1109/NaNA.2019.00055 10.1109/ICCCAS.2008.4657755 10.1016/j.cmpb.2021.106392 10.1155/2021/9293877 10.1155/2021/4028761 10.1109/CASoN.2012.6412390 10.1007/s11277-021-08565-2 10.1109/CHICC.2008.4605421 |
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| Copyright | Copyright © 2022 Shakir Khan et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Shakir Khan et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Shakir Khan et al. 2022 |
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| Title | Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms |
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