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

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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula>. <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula>, the algorithm significantly enhances our scheme's practicability. Security analyses indicate that <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> on the client side and on the blockchain is both efficient and feasible.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Cloud Computing
Published
2024-08-30
DOI
https://doi.org/10.1109/tcc.2024.3452324
Citations
3
Primary Topic
Cloud Data Security Solutions
Type
article
Field-Weighted Citation Impact
2.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Shuqin Luo, Jingting Xue, Fagen Li, Yu Zhou et al.
3 citations
IEEE Transactions on Cloud Computing
Cloud Data Security Solutions
2.34
article

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

Shuqin Luo, Jingting Xue, Fagen Li, Yu Zhou, Xiaojun Zhang, Wenzheng Zhang, Liang Liu
article en
3 citations

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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula>. <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula>, the algorithm significantly enhances our scheme's practicability. Security analyses indicate that <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">$\mathcal{AHKA}$</tex-math></inline-formula> on the client side and on the blockchain is both efficient and feasible.

IEEE Transactions on Cloud ComputingVol. 12(4)
University of Electronic Science and Technology of China (CN), Southwest Petroleum University (CN), Southwestern Institute of Physics (CN)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 9%
Cloud Data Security Solutions
2.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.