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QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute

Jie Zhang, Xiaohong Li, Mengke Zhang, Ruitao Feng, Shanshan Xu, Zhe Hou, Guangdong Bai

TL;DR

BC-ABAC faces a dual challenge: maintaining user privacy in transparent blockchain ledgers while achieving scalable, low-latency policy evaluation. The paper introduces QAE-BAC, a closed-loop framework that couples a formal $ (r,t)$-anonymity model with an Entropy-Weighted Path Tree ($EWPT$) to guide privacy-aware policy optimization, backed by cryptographic credentials and a three-module architecture (Anonymity Quantification, Dynamic Optimization, Access Control). It formalizes anonymity metrics, proves security reductions, and demonstrates on Hyperledger Fabric that the EWPT reduces policy matching to $O(m)$ time with dynamic weight updates yielding up to 11× throughput improvements and 87% latency reduction. The approach delivers a practical, scalable solution for privacy-preserving, fine-grained decentralized access control, addressing both re-identification risk and efficiency in large-scale deployments.

Abstract

Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling reidentification attacks through attribute analysis, while the computational complexity of policy matching clashes with blockchain's performance constraints. Existing solutions, such as those employing Zero-Knowledge Proofs (ZKPs), often incur high overhead and lack measurable anonymity guarantees, while efficiency optimizations frequently ignore privacy implications. To address these dual challenges, this paper proposes QAEBAC (Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute). QAE-BAC introduces a formal (r, t)-anonymity model to dynamically quantify the re-identification risk of users based on their access attributes and history. Furthermore, it features an Entropy-Weighted Path Tree (EWPT) that optimizes policy structure based on realtime anonymity metrics, drastically reducing policy matching complexity. Implemented and evaluated on Hyperledger Fabric, QAE-BAC demonstrates a superior balance between privacy and performance. Experimental results show that it effectively mitigates re-identification risks and outperforms state-of-the-art baselines, achieving up to an 11x improvement in throughput and an 87% reduction in latency, proving its practicality for privacy-sensitive decentralized applications.

QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute

TL;DR

BC-ABAC faces a dual challenge: maintaining user privacy in transparent blockchain ledgers while achieving scalable, low-latency policy evaluation. The paper introduces QAE-BAC, a closed-loop framework that couples a formal -anonymity model with an Entropy-Weighted Path Tree () to guide privacy-aware policy optimization, backed by cryptographic credentials and a three-module architecture (Anonymity Quantification, Dynamic Optimization, Access Control). It formalizes anonymity metrics, proves security reductions, and demonstrates on Hyperledger Fabric that the EWPT reduces policy matching to time with dynamic weight updates yielding up to 11× throughput improvements and 87% latency reduction. The approach delivers a practical, scalable solution for privacy-preserving, fine-grained decentralized access control, addressing both re-identification risk and efficiency in large-scale deployments.

Abstract

Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling reidentification attacks through attribute analysis, while the computational complexity of policy matching clashes with blockchain's performance constraints. Existing solutions, such as those employing Zero-Knowledge Proofs (ZKPs), often incur high overhead and lack measurable anonymity guarantees, while efficiency optimizations frequently ignore privacy implications. To address these dual challenges, this paper proposes QAEBAC (Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute). QAE-BAC introduces a formal (r, t)-anonymity model to dynamically quantify the re-identification risk of users based on their access attributes and history. Furthermore, it features an Entropy-Weighted Path Tree (EWPT) that optimizes policy structure based on realtime anonymity metrics, drastically reducing policy matching complexity. Implemented and evaluated on Hyperledger Fabric, QAE-BAC demonstrates a superior balance between privacy and performance. Experimental results show that it effectively mitigates re-identification risks and outperforms state-of-the-art baselines, achieving up to an 11x improvement in throughput and an 87% reduction in latency, proving its practicality for privacy-sensitive decentralized applications.
Paper Structure (51 sections, 1 theorem, 12 equations, 10 figures, 3 tables, 8 algorithms)

This paper contains 51 sections, 1 theorem, 12 equations, 10 figures, 3 tables, 8 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. The Evolution of Blockchain-Based Access Control
  4. The Evolution of Blockchain-Based Access Control
  5. Privacy Enhancements in Access Control
  6. Efficient Policy Management
  7. Comparison with State-of-the-Art
  8. Preliminaries
  9. Formal Model of Attribute-Based Access Control
  10. Anonymity Metrics and Quantification
  11. Cryptographic Foundation
  12. System Overview
  13. Threat Model and Design Goals
  14. Threat Model
  15. Design Goals
  16. ...and 36 more sections

Key Result

Theorem 13

Let $\lambda$ be the security parameter. Let $\mathcal{A}$ be any PPT adversary against the Request Anonymity (Def. def:request_anonymity) or Request Unlinkability (Def. def:request_unlinkability) of the QAE-BAC framwork $\Pi$, with advantage $\mathsf{Adv}_{\mathcal{A},\Pi}^{\mathsf{Anon}}(\lambda)$

Figures (10)

  • Figure 1: Architecture of the QAE-BAC framework.
  • Figure 2: Entropy-Weighted Path Tree structure: (a) initial policy organization; (b) optimized structure after dynamic weight adjustment. The tree structure enables $O(m)$ authorization time complexity where $m$ is the number of attributes.
  • Figure 3: The Impact of Subject Quantity on Anonymity Distribution
  • Figure 4: The Impact of Object Quantity on Anonymity Distribution
  • Figure 5: The Impact of Request Quantity on Anonymity Distribution
  • ...and 5 more figures

Theorems & Definitions (13)

  • Definition 1: Attribute Space
  • Definition 2: Subject
  • Definition 3: Object
  • Definition 4: Attribute Credential
  • Definition 5: Access Request
  • Definition 6: Access Policy
  • Definition 7: Subject Identifier
  • Definition 8: Credential Subject Space
  • Definition 9: Request Probability Entropy
  • Definition 10: $(r,t)$-Anonymity
  • ...and 3 more