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ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning

Sadegh Sohani, Salar Ghazi, Farnaz Kamranfar, Sahar Pilehvar Moakhar, Mohammad Allahbakhsh, Haleh Amintoosi, Kaiwen Zhang

TL;DR

ICBAC tackles the challenge of secure, adaptive access control in multi-party supply chains by integrating Hyperledger Fabric with per-channel AI anomaly detection and privacy-preserving federated learning. It introduces a multi-channel architecture, per-channel AI agents, and a hedonic coalition formation mechanism to form stable FL coalitions without revealing sensitive criteria. Dynamic access control is achieved through AI-driven anomaly detection and a permission revoke list, with promotion/demotion managed by BAC/PRL smart contracts. Empirical results show competitive blockchain performance and effective anomaly detection under IID and non-IID data while ensuring zero raw-data sharing, highlighting the framework's practical potential for privacy-preserving, decentralized SCM.

Abstract

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.

ICBAC: an Intelligent Contract-Based Access Control framework for supply chain management by integrating blockchain and federated learning

TL;DR

ICBAC tackles the challenge of secure, adaptive access control in multi-party supply chains by integrating Hyperledger Fabric with per-channel AI anomaly detection and privacy-preserving federated learning. It introduces a multi-channel architecture, per-channel AI agents, and a hedonic coalition formation mechanism to form stable FL coalitions without revealing sensitive criteria. Dynamic access control is achieved through AI-driven anomaly detection and a permission revoke list, with promotion/demotion managed by BAC/PRL smart contracts. Empirical results show competitive blockchain performance and effective anomaly detection under IID and non-IID data while ensuring zero raw-data sharing, highlighting the framework's practical potential for privacy-preserving, decentralized SCM.

Abstract

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.
Paper Structure (38 sections, 1 theorem, 25 equations, 6 figures, 3 tables, 2 algorithms)

This paper contains 38 sections, 1 theorem, 25 equations, 6 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Access Control Models
  4. Blockchain-Based Access Control Models
  5. Blockchain and Federated Learning–Based Access Control in Other Domains
  6. Discussion
  7. Framework Model Design
  8. Architecture
  9. ICBAC Components in Detail
  10. Channel and Participant Structure
  11. Asset Management Smart Contract ($\mathcal{SC}_{AM}$)
  12. Basic Access Control Smart Contract ($\mathcal{SC}_{BAC}$)
  13. Permission Revoke List Smart Contract ($\mathcal{SC}_{PRL}$)
  14. AI-Based Behavioral Anomaly Detection
  15. Behavioral Representation.
  16. ...and 23 more sections

Key Result

Theorem 1

For any friend-oriented federated learning problem, the SCC partition $\pi^{SCC}$ satisfies: (i) core stability, (ii) group strategy-proofness, and (iii) computability in $O(|N|+|A|)$ time via Tarjan’s algorithm.

Figures (6)

  • Figure 1: Overall architecture of the proposed ICBAC framework.
  • Figure 2: UML diagram of the overall workflow of asset updates and access control within a supply chain channel.
  • Figure 3: Latency and throughput analysis of ICBAC smart contract functions.
  • Figure 4: Comparative performance analysis of ICBAC against recent static blockchain-based access control models in SCM.
  • Figure 5: Performance metrics for IID dataset across different coalitions.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Definition 1: Hedonic Coalition Formation
  • Definition 2: Core Stability
  • Definition 3: Strategy-Proofness
  • Definition 4: Friend-Oriented Preferences
  • Theorem 1: SCC Partition Properties