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Towards Dynamic Verifiable Pattern Matching

delete2021-06-01
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PRE
AI
陈
陈飞 (Fei Chen) *
W
Wang Dong-hong
林
林秋镇 (Qiuzhen Lin)
陈健勇 封面图
陈健勇 (Jianyong Chen)
Z
Zhong Ming
Wei Yu 封面图
Wei Yu (Wei Yu)
J
Jing Qin
DOI:10.1109/TBDATA.2018.2868657delete
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摘要

摘要

En 中文
Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.
Keyword:
Pattern matching
Servers
Indexes
Testing
Data structures
Heuristic algorithms
Big data
pattern matching
verifiability
data dynamics
computation outsourcing
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期刊

I
IEEE Transactions on Big Data
IF:
5.7
论文数:
887
被引数:
3.0K

机构

T
Towson University
学者数:
750
论文数: 665
被引数: 1.2K
S
shenzhen university
学者数:
4.6W
论文数: 3.4W
被引数: 72
University System of Maryland 封面图
University System of Maryland
学者数:
6.5W
论文数: 5.6W
被引数: 113
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