## 6 Conclusions and Future Work  
This paper presented a modular, blockchain-based access control framework that integrates ABAC with smart contracts to manage access to tokenized mining licenses. Deployed on Hyperledger Fabric and validated through structured simulation, the system demonstrated robust correctness by enforcing over 1500 access decisions in full compliance with defined policies. The framework provided fine-grained control, granting access to only 23.7% of requests based on dynamic attribute conditions, thereby mitigating the risk of unauthorized exposure. Performance testing indicated the system could sustain over 100 access evaluations per minute with sub-40 ms latency, confirming its responsiveness for real-world applications. Furthermore, the use of structured JSON inputs and CSV logs ensured auditability and readiness for integration with AI and data analytics pipelines.  
Looking ahead, future research can expand this work through large-scale stress testing, including adversarial simulations and distributed peer deployments to assess fault tolerance. Such stress testing under adverse network conditions and peer failures remains to be conducted, as this study focused on controlled environments with stable parameters. Live pilot trials in partnership with industry or regulators will provide opportunities for field validation. Enhancements such as incorporating contextual conditions (e.g., geolocation or time-based rules), integrating privacy-preserving mechanisms like zero-knowledge proofs, and leveraging structured logs for AI-based policy optimization  
Figure 10: Access Matrix (Users \(\times\) Resources).  
and anomaly detection represent promising directions to extend the system's utility in compliance-critical environments.  
The current framework incorporates foundational access control aligned with privacy principles, though detailed consideration of cross-border regulations and ethical frameworks is reserved for future work  
Conceptualization, P.N. and H.C.; Methodology, P.N. and H.C.; Validation, P.N. and H.C.; Formal analysis, P.N.; Investigation, P.N. and B.C.; Resources, P.N., B.C. and H.C.; Writing--original draft, P.N.; Writing--review & editing, H.C.; Visualization, P.N.; Supervision, B.C. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
During the development of the simulation environment, the authors used ChatGPT (OpenAI GPT-4, 2025 version) for the purposes of learning foundational concepts related to ABAC, Hyperledger Fabric architecture, and simulation setup strategies. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABAC & Attribute-Based Access Control \\ RBAC & Role-Based Access Control \\ IoT & Internet of Things \\ AI & Artificial Intelligence \\ ML & Machine Learning \\ TLS & Transport Layer Security \\ CLI & Command-Line Interface \\ CA & Certificate Authority \\ JSON & JavaScript Object Notation \\ CSV & Comma-Separated Values \\ MSP & Membership Service Provider \\ URL & Uniform Resource Locator \\ ZKPs & Zero-Knowledge Proofs \\ SMPC & Secure Multi-Party Computation \\ Go & Go Programming Language \\ Docker & (Platform for containerized application deployment) \\ \end{tabular}