[Paper] Fuzzychain-edge: A novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing

Source: arXiv - 2601.10105v1

Overview

The paper introduces Fuzzychain‑edge, a hybrid security framework that blends fuzzy‑logic‑driven access control, Zero‑Knowledge Proofs (ZKPs), and blockchain smart contracts to protect IoT data flowing through edge‑computing nodes. By making access decisions context‑aware and privacy‑preserving, the authors aim to close the gap left by traditional, static, and centralized security models—especially in high‑stakes domains like healthcare.

Key Contributions

• Adaptive fuzzy‑logic access control that evaluates data sensitivity, trust scores, and user roles in real time.
• Integration of Zero‑Knowledge Proofs to verify user credentials without exposing any private attributes.
• Blockchain‑backed audit trail using smart contracts for immutable logging and automated enforcement of access policies.
• End‑to‑end prototype architecture that demonstrates how edge devices, a permissioned blockchain, and a fuzzy inference engine can interoperate securely.
• Security analysis showing resistance to common attacks (e.g., replay, impersonation, and insider threats) while preserving low latency suitable for edge environments.

Methodology

1. System Design – The authors sketch a three‑layer architecture: (a) IoT/edge layer (sensors, actuators), (b) fuzzy inference layer (runs on edge gateways), and (c) a permissioned blockchain network that hosts smart contracts.
2. Fuzzy Inference Engine – Input variables (data sensitivity, user trust level, role hierarchy) are fuzzified into linguistic terms (e.g., high, medium, low). A rule base (≈ 15 rules) produces a crisp “access score” that determines permit/deny.
3. Zero‑Knowledge Proof Module – Before the fuzzy engine runs, the requester submits a ZKP that proves possession of required attributes (e.g., certification, clearance) without revealing them. The verifier checks the proof using standard zk‑SNARK constructions.
4. Smart Contract Enforcement – The blockchain stores the fuzzy decision and the ZKP verification result. Smart contracts automatically grant or revoke access, and every decision is recorded immutably for audit.
5. Evaluation – The prototype is deployed on a Raspberry‑Pi edge gateway, Hyperledger Fabric as the blockchain, and a Python‑based fuzzy engine. Latency, throughput, and security properties are measured against a baseline static ACL system.

Results & Findings

Interpretation: While the added cryptographic and fuzzy processing introduces modest overhead (≈ 30 ms per request), the framework dramatically cuts unauthorized access incidents and virtually eliminates successful data‑leakage attempts. The immutable audit log also enables post‑mortem forensics without impacting runtime performance.

Practical Implications

• Healthcare IoT – Hospitals can safely expose patient vitals to edge analytics while guaranteeing that only verified clinicians (proved via ZKPs) obtain the data, satisfying HIPAA‑like regulations.
• Edge‑First Enterprises – Companies deploying smart factories or autonomous vehicles can embed the fuzzy engine on gateways, allowing policies that adapt to changing trust (e.g., a device’s firmware health) without manual re‑configuration.
• Developer Tooling – The smart‑contract templates and fuzzy‑rule DSL are open‑source, making it straightforward to plug the model into existing Hyperledger or Ethereum‑compatible stacks.
• Regulatory Compliance – Immutable blockchain logs provide auditors with tamper‑proof evidence of who accessed what and when, simplifying compliance reporting.

Limitations & Future Work

• Scalability – The current prototype runs on a permissioned blockchain with a modest number of nodes; performance under large consortiums remains untested.
• Rule Management – Fuzzy rule creation still requires domain expertise; automating rule learning from historical access patterns is an open challenge.
• ZKP Overhead – Although lightweight zk‑SNARKs were used, generating proofs on constrained IoT devices may be prohibitive; future work will explore hardware‑accelerated ZKP schemes.
• Interoperability – Integration with public blockchains or heterogeneous edge platforms (e.g., Kubernetes‑based edge clusters) needs further engineering.

Bottom line: Fuzzychain‑edge demonstrates that a thoughtfully combined stack of fuzzy logic, zero‑knowledge proofs, and blockchain can deliver adaptive, privacy‑preserving access control for edge‑centric IoT systems—opening a path for developers to build more trustworthy, audit‑ready applications in sensitive domains.

Authors

• Khushbakht Farooq
• Muhammad Ibrahim
• Irsa Manzoor
• Mukhtaj Khan
• Wei Song

Paper Information

• arXiv ID: 2601.10105v1
• Categories: cs.CR, cs.DC
• Published: January 15, 2026
• PDF: Download PDF