A secure restful deployment pattern for course recommendation microservices using proof-of-work blockchain authentication

This is the authors' abstract. We don't add key points for this paper.

Deploying machine learning models across distributed environments requires robust architectural patterns that ensure both cross-platform interoperability and secure access control. While Representational State Transfer (REST) APIs are widely adopted for service exposure, existing implementations in cross-machine scenarios remain vulnerable to token spoofing and post-hoc administrative tampering of centralized authorization logs. Therefore, this study presents a secure, deployable architectural pattern that integrates localized machine learning workflows with a decentralized authentication boundary. An online course recommendation system using text preprocessing, Term Frequency–Inverse Document Frequency (TF–IDF) vectorization, and cosine similarity is implemented and utilized explicitly as a functional demonstration case to validate the framework. The core machine learning engine is exposed via a Flask-implemented RESTful service layer, while secure access control is enforced by embedding a Proof-of-Work (PoW) blockchain token authentication mechanism at the API boundary. The empirical results demonstrate that access to protected endpoints is granted only upon successful validation of user tokens against immutable on-chain ledger records, structurally rejecting unauthorized requests. Performance benchmarking indicates that while the decentralized ledger operations introduce an operational latency trade-off (a 1113 ms mining overhead and 2.45-second end-to-end authorized query response), the proposed pattern provides an effective, tamper-evident firewall for delivering secure microservices in distributed client–server environments.