Attribute-Based Hierarchical Keyword Auditing With Batch Fault Localization Assisted by Smart Contracts

Abstract

Keyword-based auditing (KA) provides a means for users to verify the integrity of only the outsourced data they are interested in. Existing KA schemes employ relation authentication labels to conduct targeted audits with keywords, which significantly improves the cost-effectiveness. However, such schemes typically support only a single-challenge scenario, which may not always be practical. To overcome this constraint, we introduce a hierarchical challenge mechanism grounded in user attributes. This mechanism leverages inequality and affiliation relationships to comply with a predefined tree structure for access policies. Incorporated during the challenge-response phase of the auditing model, it permits users to initiate cross-challenges. Expanding upon this hierarchical mechanism, we propose an attribute-based hierarchical keyword auditing scheme, abbreviated as $\mathcal{AHKA}$ . $\mathcal{AHKA}$ combines searchable encryption to conduct cross-targeted audits and benefits from the hash collision mapping of Bloom filters to safeguard against keyword guessing attacks. Moreover, we design a fault localization algorithm based on a variant of the binary search technique. It locates in batch the faulty cloud servers and damaged data blocks after an audit failure. As an integral part of $\mathcal{AHKA}$ , the algorithm significantly enhances our scheme's practicability. Security analyses indicate that $\mathcal{AHKA}$ can effectively withstand both forgery and replace attacks on audit proofs. The smart contract component ensures that our scheme's processes can be monitored and regulated. Experimental data corroborate that deploying $\mathcal{AHKA}$ on the client side and on the blockchain is both efficient and feasible.