Privacy Vulnerabilities in Multi-Cloud Machine Learning: Five Integrated Theoretical Frameworks for Systematic Understanding, Assessment, and Mitigation
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901816136957952 |
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| author | Tahchiev, Andrean |
| author_facet | Tahchiev, Andrean |
| contents | <p>Privacy vulnerabilities in cloud-based machine learning systems represent a critical challenge in modern distributed computing environments. This paper presents five integrated theoretical frameworks for systematically understanding and mitigating privacy vulnerabilities in multi-cloud ML environments: (1) a Three-Dimensional Vulnerability Model providing formal mathematical risk categorisation with 87% retrospective classification accuracy; (2) a Protection Mechanism Interaction Framework quantifying synergistic and interfering mechanism combinations; (3) a Deployment-Risk Taxonomy connecting four deployment archetypes to distinct vulnerability profiles covering 92.6% of documented incidents; (4) a multi-objective Protection Selection Model achieving 77% alignment with expert decision analyses; and (5) a standardised Evaluation Metrics Framework. The research employs critical realist methodology, analysing 27 documented privacy incidents. All validation criteria were exceeded across three independent validation phases.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18979816 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Privacy Vulnerabilities in Multi-Cloud Machine Learning: Five Integrated Theoretical Frameworks for Systematic Understanding, Assessment, and Mitigation Tahchiev, Andrean machine learning privacy, cloud computing, multi-cloud, differential privacy, membership inference, vulnerability assessment <p>Privacy vulnerabilities in cloud-based machine learning systems represent a critical challenge in modern distributed computing environments. This paper presents five integrated theoretical frameworks for systematically understanding and mitigating privacy vulnerabilities in multi-cloud ML environments: (1) a Three-Dimensional Vulnerability Model providing formal mathematical risk categorisation with 87% retrospective classification accuracy; (2) a Protection Mechanism Interaction Framework quantifying synergistic and interfering mechanism combinations; (3) a Deployment-Risk Taxonomy connecting four deployment archetypes to distinct vulnerability profiles covering 92.6% of documented incidents; (4) a multi-objective Protection Selection Model achieving 77% alignment with expert decision analyses; and (5) a standardised Evaluation Metrics Framework. The research employs critical realist methodology, analysing 27 documented privacy incidents. All validation criteria were exceeded across three independent validation phases.</p> |
| title | Privacy Vulnerabilities in Multi-Cloud Machine Learning: Five Integrated Theoretical Frameworks for Systematic Understanding, Assessment, and Mitigation |
| topic | machine learning privacy, cloud computing, multi-cloud, differential privacy, membership inference, vulnerability assessment |
| url | https://doi.org/10.5281/zenodo.18979816 |