Secure and Scalable LMS Design for Lifelong Learning

Authors

  • Isabella Horvath Senior Lecturer, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia Author
  • Clara Klein Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain Author

Keywords:

LMS security, lifelong learning, zero-trust architecture, post-quantum cryptography, privacy-preserving analytics, decentralised identity, GDPR compliance, scalable LMS

Abstract

Lifelong learning -- the continuous, voluntary acquisition of knowledge and skills across a person's entire career and life -- demands Learning Management Systems that span institutional boundaries, persist across decades, and maintain security and privacy for increasingly sensitive learner records that accumulate over time. Unlike institutional LMS deployments bounded by enrolment periods, lifelong learning platforms must manage learner records across multiple employers, institutions, and credentialing bodies; protect data under multiple overlapping regulatory regimes (GDPR, national data protection laws, sector-specific regulations); and scale elastically to accommodate global learner populations without compromising individual privacy. This paper proposes the Secure Lifelong Learning System Architecture (SLLSA), a comprehensive design framework addressing three dimensions: security architecture (zero-trust security model, post-quantum cryptography migration, privacy-preserving analytics), scalability design (multi-tenant cloud architecture, horizontal scaling, global content delivery), and lifelong record management (decentralised identity, verifiable credentials, right-to-erasure compliance). SLLSA is validated through design review by 18 educational technology security experts and prototype deployment evaluation at two lifelong learning platforms. Key results: zero-trust architecture reduces unauthorised access incidents by 94.2%; PQC migration adds 18.4 ms TLS overhead; privacy-preserving analytics preserves 92.4% of learning insight utility at full differential privacy protection. The framework provides a security and scalability blueprint for lifelong learning platform architects.

Author Biographies

  • Isabella Horvath, Senior Lecturer, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia

    Senior Lecturer, Department of Artificial Intelligence, Baltic AI Research University, Tallinn, Estonia

  • Clara Klein, Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain

    Assistant Professor, Department of Computer Science, Western Europe Data Science University, Madrid, Spain

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Published

2025-06-28

How to Cite

Secure and Scalable LMS Design for Lifelong Learning. (2025). Journal of Digital Learning Futures P-ISSN 3117-6054 and E-ISSN 3117-6062, 2(2), 19-26. https://galaxiauniverse.com/index.php/JDLF/article/view/390