QAE-BAC: Achieving Quantifiable Anonymity and Efficiency in Blockchain-Based Access Control with Attribute
For developers of privacy-sensitive decentralized applications, QAE-BAC provides a practical solution that balances quantifiable anonymity with high performance, addressing a critical bottleneck in blockchain access control.
QAE-BAC addresses the dual challenges of privacy and performance in blockchain-based attribute-based access control by introducing a formal (r, t)-anonymity model to quantify re-identification risk and an Entropy-Weighted Path Tree to optimize policy matching. Evaluated on Hyperledger Fabric, it achieves up to 11x throughput improvement and 87% latency reduction while mitigating re-identification risks.
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.