A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment
Abstract The rapid adoption of cloud-based data sharing is transforming collaboration across various sectors, yet ensuring trust and privacy in sensitive data remains a critical challenge. This paper presents a hybrid model aimed at enhancing data privacy and trust in cloud environments, specificall...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-11-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-024-00717-6 |
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| _version_ | 1850107531046682624 |
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| author | Himani Saini Gopal Singh Sandeep Dalal Iyyappan Moorthi Sultan Mesfer Aldossary Nasratullah Nuristani Arshad Hashmi |
| author_facet | Himani Saini Gopal Singh Sandeep Dalal Iyyappan Moorthi Sultan Mesfer Aldossary Nasratullah Nuristani Arshad Hashmi |
| author_sort | Himani Saini |
| collection | DOAJ |
| description | Abstract The rapid adoption of cloud-based data sharing is transforming collaboration across various sectors, yet ensuring trust and privacy in sensitive data remains a critical challenge. This paper presents a hybrid model aimed at enhancing data privacy and trust in cloud environments, specifically addressing concerns in healthcare and finance. The model combines k-anonymity for user privacy, an optimized Firefly algorithm for trust generation, and a Time-aware Modified Best Fit Decreasing (T-MBFD) algorithm to improve resource allocation efficiency. Key contributions include a comprehensive methodology that encompasses dataset selection, preprocessing, model training, and evaluation across multiple datasets, including healthcare, financial, and pandemic-related data. Experimental results demonstrate that the hybrid model achieves a precision score of approximately 90% and an accuracy of around 93% in financial datasets, significantly outperforming existing methods in both privacy preservation and computational efficiency. These findings emphasize the model’s effectiveness in securely facilitating data-driven collaboration in highly regulated domains, thus paving the way for practical applications that uphold individual privacy and data integrity in cloud-based environments. |
| format | Article |
| id | doaj-art-49d149bc458f4f3092ac2b6862fc0ff9 |
| institution | OA Journals |
| issn | 2192-113X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj-art-49d149bc458f4f3092ac2b6862fc0ff92025-08-20T02:38:33ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-11-0113113110.1186/s13677-024-00717-6A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environmentHimani Saini0Gopal Singh1Sandeep Dalal2Iyyappan Moorthi3Sultan Mesfer Aldossary4Nasratullah Nuristani5Arshad Hashmi6Department of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Applications, Maharshi Dayanand UniversityDepartment of Computer Science & Applications, Maharshi Dayanand UniversityCollege of Information Technology, Ahlia University Manama, Kingdom of BahrainComputer Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz UniversityDepartment of Spectrum Management, Telecommunication Regulatory AuthorityDepartment of Information Systems, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz UniversityAbstract The rapid adoption of cloud-based data sharing is transforming collaboration across various sectors, yet ensuring trust and privacy in sensitive data remains a critical challenge. This paper presents a hybrid model aimed at enhancing data privacy and trust in cloud environments, specifically addressing concerns in healthcare and finance. The model combines k-anonymity for user privacy, an optimized Firefly algorithm for trust generation, and a Time-aware Modified Best Fit Decreasing (T-MBFD) algorithm to improve resource allocation efficiency. Key contributions include a comprehensive methodology that encompasses dataset selection, preprocessing, model training, and evaluation across multiple datasets, including healthcare, financial, and pandemic-related data. Experimental results demonstrate that the hybrid model achieves a precision score of approximately 90% and an accuracy of around 93% in financial datasets, significantly outperforming existing methods in both privacy preservation and computational efficiency. These findings emphasize the model’s effectiveness in securely facilitating data-driven collaboration in highly regulated domains, thus paving the way for practical applications that uphold individual privacy and data integrity in cloud-based environments.https://doi.org/10.1186/s13677-024-00717-6Time-aware modified best fit decreasing (T-MBFD)Resource allocationPrivacy preservationTrust generationState-of-the-art algorithms |
| spellingShingle | Himani Saini Gopal Singh Sandeep Dalal Iyyappan Moorthi Sultan Mesfer Aldossary Nasratullah Nuristani Arshad Hashmi A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment Journal of Cloud Computing: Advances, Systems and Applications Time-aware modified best fit decreasing (T-MBFD) Resource allocation Privacy preservation Trust generation State-of-the-art algorithms |
| title | A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment |
| title_full | A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment |
| title_fullStr | A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment |
| title_full_unstemmed | A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment |
| title_short | A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment |
| title_sort | hybrid machine learning model with self improved optimization algorithm for trust and privacy preservation in cloud environment |
| topic | Time-aware modified best fit decreasing (T-MBFD) Resource allocation Privacy preservation Trust generation State-of-the-art algorithms |
| url | https://doi.org/10.1186/s13677-024-00717-6 |
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