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Efficient Deterministic Verification and Rapid Corruption Localization for Edge Data Integrity

delete2024-10-15
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PRE
AI
J
Jian Li
赵庆林 (Qinglin Zhao) *
S
Shaohua Teng
N
Naiqi Wu
P
Peiyun Zhang
DOI:10.1109/JIOT.2024.3425905delete
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摘要

摘要

En 中文
Ensuring data integrity in edge computing environments presents significant challenges, primarily due to the distributed architecture of edge servers and the inherent risk of data corruption. Traditional edge data integrity (EDI) verification methods predominantly rely on sampling techniques, provide only probabilistic integrity assurances and often struggle with scalability and efficient corruption localization. To overcome these limitations, we introduce the deterministic integrity assurance and rapid corruption localization EDI (DL-EDI) verification scheme, a novel approach that combines extended Merkle grid (EM-Grid) with Boneh-Lynn-Shacham (BLS) signatures. DL-EDI leverages EM-Grid for comprehensive data integrity verification, ensuring deterministic integrity validation across all data blocks while facilitating rapid block corruption localization. Additionally, we incorporate a hierarchical signature aggregation method using BLS signatures to optimize verification efficiency and minimize communication and computational overhead. A thorough performance analysis of DL-EDI is conducted, evaluating its verification accuracy, communication and computational efficiency, and resilience against various security threats. Comparative experimental evaluations of DL-EDI against four established EDI schemes highlight its superior effectiveness and efficiency in addressing the challenges of EDI.
Keyword:
Servers
Location awareness
Data integrity
Probabilistic logic
Edge computing
Maintenance engineering
Low latency communication
Batch verification
corruption localization
deterministic assurance
edge data integrity (EDI)

期刊

IEEE Internet of Things Journal 封面图
IEEE Internet of Things Journal
IF:
8.9
论文数:
1.4W
被引数:
7.8W

机构

G
guangdong university of technology
学者数:
3.0W
论文数: 2.0W
被引数: 36
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