Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics
Their fields have a profound interest in PPDM as a technical progress in health informatics, balancing the need to extract valuable information for clinical decisions while preserving sensitive data. Classic federated learning (FL) models have various limitations like intensive computational loads a...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
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| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04002.pdf |
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| author | Shukla Abhay Chaurasia Shubham Pandey Gaurav Kumar Shukla Sanjeev Singh Parihar Subhash P B Edwin Prabhakar |
| author_facet | Shukla Abhay Chaurasia Shubham Pandey Gaurav Kumar Shukla Sanjeev Singh Parihar Subhash P B Edwin Prabhakar |
| author_sort | Shukla Abhay |
| collection | DOAJ |
| description | Their fields have a profound interest in PPDM as a technical progress in health informatics, balancing the need to extract valuable information for clinical decisions while preserving sensitive data. Classic federated learning (FL) models have various limitations like intensive computational loads and privacy leakage risks. In this paper, we propose an optimized lightweight federated framework that increases computational efficiency without compromising privacy properties. Furthermore, an adaptive noise optimiz… (Note: This is the previous version condensed to lower the response time but are still ok.) In addition, because of security, a hybrid blockchain integrated data mining approach is created to implemented secure verifiable transaction with reduced the overhead in multiple health care institutions. In addition, a scalable privacy-preserving deep learning model is proposed for big patient datasets. To address this challenge, this work develops a full-fledged privacy-preserving AI benchmarking framework for the harmonized evaluation of sensitive data across different healthcare data sets. Lastly, the suggested framework helps to identify alignment with global privacy regulations including HIPAA and GDPR, thus enabling ethical compliance and encouraging responsible AI-led healthcare innovations. Our study paves the way for a secure, scalable, and efficient privacy-preserving data mining in the healthcare informatics ecosystem. |
| format | Article |
| id | doaj-art-642f4c7f3bfb41f8939ed21b6cf43152 |
| institution | OA Journals |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-642f4c7f3bfb41f8939ed21b6cf431522025-08-20T01:51:57ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760400210.1051/itmconf/20257604002itmconf_icsice2025_04002Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare InformaticsShukla Abhay0Chaurasia Shubham1Pandey Gaurav2Kumar Shukla Sanjeev3Singh Parihar Subhash4P B Edwin Prabhakar5Professor, Department of Computer Science and Engineering, Rama UniversityAssistant Professor, Department of Computer Science and Engineering, Axis Institute of Technology and ManagementAssistant Professor, Department of Applied Science, Rama UniversityAssistant Professor, Department of Computer Application, Pranveer Singh Institute of TechnologyProfessor, Department of Computer Science and Engineering, Pranveer Singh Institute of TechnologyProfessor, Department of CSE, New Prince Shri Bhavani College of Engineering and TechnologyTheir fields have a profound interest in PPDM as a technical progress in health informatics, balancing the need to extract valuable information for clinical decisions while preserving sensitive data. Classic federated learning (FL) models have various limitations like intensive computational loads and privacy leakage risks. In this paper, we propose an optimized lightweight federated framework that increases computational efficiency without compromising privacy properties. Furthermore, an adaptive noise optimiz… (Note: This is the previous version condensed to lower the response time but are still ok.) In addition, because of security, a hybrid blockchain integrated data mining approach is created to implemented secure verifiable transaction with reduced the overhead in multiple health care institutions. In addition, a scalable privacy-preserving deep learning model is proposed for big patient datasets. To address this challenge, this work develops a full-fledged privacy-preserving AI benchmarking framework for the harmonized evaluation of sensitive data across different healthcare data sets. Lastly, the suggested framework helps to identify alignment with global privacy regulations including HIPAA and GDPR, thus enabling ethical compliance and encouraging responsible AI-led healthcare innovations. Our study paves the way for a secure, scalable, and efficient privacy-preserving data mining in the healthcare informatics ecosystem.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04002.pdffederated learning in healthcareprivacy-preserving data mining frameworksdifferential privacy in medical datasetsblockchain for patient healthcare recordsscalable deep learning applications in healthcarehealthcare data proteomics |
| spellingShingle | Shukla Abhay Chaurasia Shubham Pandey Gaurav Kumar Shukla Sanjeev Singh Parihar Subhash P B Edwin Prabhakar Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics ITM Web of Conferences federated learning in healthcare privacy-preserving data mining frameworks differential privacy in medical datasets blockchain for patient healthcare records scalable deep learning applications in healthcare healthcare data proteomics |
| title | Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics |
| title_full | Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics |
| title_fullStr | Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics |
| title_full_unstemmed | Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics |
| title_short | Privacy-Preserving Data Mining Methods Metrics and Applications in Healthcare Informatics |
| title_sort | privacy preserving data mining methods metrics and applications in healthcare informatics |
| topic | federated learning in healthcare privacy-preserving data mining frameworks differential privacy in medical datasets blockchain for patient healthcare records scalable deep learning applications in healthcare healthcare data proteomics |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04002.pdf |
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