Privacy-Preserving Threat Intelligence Sharing Across Organizational Boundaries
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| Autores principales: | , |
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| Formato: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866902111806029824 |
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| author | Marawa Ali Safa Mohmaed |
| author_facet | Marawa Ali Safa Mohmaed |
| contents | <p class="MsoNormal">This article presents a comprehensive examination of privacy-preserving threat intelligence sharing across organizational boundaries, addressing the critical challenges and opportunities at the intersection of cybersecurity, advanced system architecture, and cybersecurity. The study synthesizes insights from peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on the technical and organizational dimensions of modern security. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in cybersecurity.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20392209 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Privacy-Preserving Threat Intelligence Sharing Across Organizational Boundaries Marawa Ali Safa Mohmaed <p class="MsoNormal">This article presents a comprehensive examination of privacy-preserving threat intelligence sharing across organizational boundaries, addressing the critical challenges and opportunities at the intersection of cybersecurity, advanced system architecture, and cybersecurity. The study synthesizes insights from peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on the technical and organizational dimensions of modern security. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in cybersecurity.</p> |
| title | Privacy-Preserving Threat Intelligence Sharing Across Organizational Boundaries |
| url | https://doi.org/10.5281/zenodo.20392209 |