Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model
The urgent and indispensable requirement for secure and efficient remote work compels organizations to reconsider their approach to safeguarding themselves against cyber threats. The shift toward remote work amplifies the need to redirect more network traffic toward cloud-based applications rather t...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2023.2219560 |
| _version_ | 1797684812515901440 |
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| author | Yitian Zhang |
| author_facet | Yitian Zhang |
| author_sort | Yitian Zhang |
| collection | DOAJ |
| description | The urgent and indispensable requirement for secure and efficient remote work compels organizations to reconsider their approach to safeguarding themselves against cyber threats. The shift toward remote work amplifies the need to redirect more network traffic toward cloud-based applications rather than relying solely on the internal network. The growing adoption of the hybrid work model necessitates system administrators to increasingly provide access to applications and services beyond the conventional boundaries of enterprise networks. Ensuring privacy goes beyond mere compliance with regulations; it is crucial for demonstrating transparency and accountability, which are essential in building trust with stakeholders. Employing a zero-trust approach can proactively enhance privacy by implementing access controls based on the principle of least privilege and predefined purposes. Such an approach helps to limit potential damages and enhances the resilience of complex information systems. This work proposes an innovative privacy-preserving and zero-trust computational intelligent hybrid system. Building upon the zero-trust architecture, this system ensures a protected environment while preserving privacy. It achieves this by employing multi-level trust fields within a corporate network, where every access request undergoes comprehensive authentication, authorization, and encryption before being granted access. The system’s efficacy is validated within a sports training application environment, with stringent authorization requirements and the corresponding need to safeguard personal privacy. By implementing the proposed system, the application environment can effectively mitigate privacy risks while providing secure access only to authorized individuals. The hybrid system’s computational intelligence further enhances its ability to adapt to evolving threats and maintain the confidentiality and integrity of sensitive data. In summary, the current landscape of remote work necessitates organizations to prioritize cybersecurity and privacy. By embracing a zero-trust approach and implementing the privacy-preserving and zero-trust computational intelligent hybrid system, organizations can ensure robust protection, maintain privacy compliance, and establish a trusted foundation for remote work environments. |
| first_indexed | 2024-03-12T00:35:12Z |
| format | Article |
| id | doaj.art-798823701ff64173824bf8e1e7a799d1 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:35:12Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-798823701ff64173824bf8e1e7a799d12023-09-15T10:01:06ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452023-12-0137110.1080/08839514.2023.22195602219560Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education ModelYitian Zhang0Zhengzhou Preschool Education CollegeThe urgent and indispensable requirement for secure and efficient remote work compels organizations to reconsider their approach to safeguarding themselves against cyber threats. The shift toward remote work amplifies the need to redirect more network traffic toward cloud-based applications rather than relying solely on the internal network. The growing adoption of the hybrid work model necessitates system administrators to increasingly provide access to applications and services beyond the conventional boundaries of enterprise networks. Ensuring privacy goes beyond mere compliance with regulations; it is crucial for demonstrating transparency and accountability, which are essential in building trust with stakeholders. Employing a zero-trust approach can proactively enhance privacy by implementing access controls based on the principle of least privilege and predefined purposes. Such an approach helps to limit potential damages and enhances the resilience of complex information systems. This work proposes an innovative privacy-preserving and zero-trust computational intelligent hybrid system. Building upon the zero-trust architecture, this system ensures a protected environment while preserving privacy. It achieves this by employing multi-level trust fields within a corporate network, where every access request undergoes comprehensive authentication, authorization, and encryption before being granted access. The system’s efficacy is validated within a sports training application environment, with stringent authorization requirements and the corresponding need to safeguard personal privacy. By implementing the proposed system, the application environment can effectively mitigate privacy risks while providing secure access only to authorized individuals. The hybrid system’s computational intelligence further enhances its ability to adapt to evolving threats and maintain the confidentiality and integrity of sensitive data. In summary, the current landscape of remote work necessitates organizations to prioritize cybersecurity and privacy. By embracing a zero-trust approach and implementing the privacy-preserving and zero-trust computational intelligent hybrid system, organizations can ensure robust protection, maintain privacy compliance, and establish a trusted foundation for remote work environments.http://dx.doi.org/10.1080/08839514.2023.2219560 |
| spellingShingle | Yitian Zhang Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model Applied Artificial Intelligence |
| title | Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model |
| title_full | Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model |
| title_fullStr | Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model |
| title_full_unstemmed | Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model |
| title_short | Privacy-Preserving with Zero Trust Computational Intelligent Hybrid Technique to English Education Model |
| title_sort | privacy preserving with zero trust computational intelligent hybrid technique to english education model |
| url | http://dx.doi.org/10.1080/08839514.2023.2219560 |
| work_keys_str_mv | AT yitianzhang privacypreservingwithzerotrustcomputationalintelligenthybridtechniquetoenglisheducationmodel |