Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.08014 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914314458234880 |
|---|---|
| author | Sohani, Sadegh Ghazi, Salar Kamranfar, Farnaz Moakhar, Sahar Pilehvar Allahbakhsh, Mohammad Amintoosi, Haleh Zhang, Kaiwen |
| author_facet | Sohani, Sadegh Ghazi, Salar Kamranfar, Farnaz Moakhar, Sahar Pilehvar Allahbakhsh, Mohammad Amintoosi, Haleh Zhang, Kaiwen |
| contents | This paper addresses the critical challenge of access control in modern supply chains, which operate across multiple independent and competing organizations. Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts. Blockchain improves decentralization but lacks behavioral intelligence, while centralized machine learning for anomaly detection requires aggregating sensitive data, violating privacy.
The proposed solution is ICBAC, an intelligent contract-based access control framework. It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL). Built on Fabric, ICBAC uses a multi-channel architecture and three smart contracts for asset management, baseline access control, and dynamic revocation. To counter insider misuse, each channel deploys an AI agent that monitors activity and dynamically restricts access for anomalies. Federated learning allows these agents to collaboratively improve detection models without sharing raw data.
For heterogeneous, competitive environments, ICBAC introduces a game-theoretic client selection mechanism using hedonic coalition formation. This enables supply chains to form stable, strategy-proof FL coalitions via preference-based selection without disclosing sensitive criteria. Extensive experiments on a Fabric testbed with a real-world dataset show ICBAC achieves blockchain performance comparable to static frameworks and provides effective anomaly detection under IID and non-IID data with zero raw-data sharing. ICBAC thus offers a practical, scalable solution for dynamic, privacy-preserving access control in decentralized supply chains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08014 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning Sohani, Sadegh Ghazi, Salar Kamranfar, Farnaz Moakhar, Sahar Pilehvar Allahbakhsh, Mohammad Amintoosi, Haleh Zhang, Kaiwen Cryptography and Security Artificial Intelligence H.4; I.2 This paper addresses the critical challenge of access control in modern supply chains, which operate across multiple independent and competing organizations. Existing access control is static and centralized, unable to adapt to insider threats or evolving contexts. Blockchain improves decentralization but lacks behavioral intelligence, while centralized machine learning for anomaly detection requires aggregating sensitive data, violating privacy. The proposed solution is ICBAC, an intelligent contract-based access control framework. It integrates permissioned blockchain (Hyperledger Fabric) with federated learning (FL). Built on Fabric, ICBAC uses a multi-channel architecture and three smart contracts for asset management, baseline access control, and dynamic revocation. To counter insider misuse, each channel deploys an AI agent that monitors activity and dynamically restricts access for anomalies. Federated learning allows these agents to collaboratively improve detection models without sharing raw data. For heterogeneous, competitive environments, ICBAC introduces a game-theoretic client selection mechanism using hedonic coalition formation. This enables supply chains to form stable, strategy-proof FL coalitions via preference-based selection without disclosing sensitive criteria. Extensive experiments on a Fabric testbed with a real-world dataset show ICBAC achieves blockchain performance comparable to static frameworks and provides effective anomaly detection under IID and non-IID data with zero raw-data sharing. ICBAC thus offers a practical, scalable solution for dynamic, privacy-preserving access control in decentralized supply chains. |
| title | ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning |
| topic | Cryptography and Security Artificial Intelligence H.4; I.2 |
| url | https://arxiv.org/abs/2602.08014 |