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A blockchain-based log storage model with efficient query

delete2023-07-24
delete5
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OA
AI
徐刚 (Gang Xu)
F
Fan Yun
S
Shiyuan Xu
Y
Yiying Yu
X
Xiu‐Bo Chen *
M
Mianxiong Dong
DOI:10.1007/s00500-023-08975-3delete
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Abstract

Abstract

En 中文
Server logs are always essential in website maintenance. The services maintained may encounter various problems, and the logs generated by the server are the core data for locating the problem. However, existing log storage systems are susceptible to single points of failure, which can lead to potential log leakage. Meanwhile, external adversaries can tamper with logs to avoid tracking, and administrators can also tamper with logs to evade responsibility. In order to solve these tough shortcomings effectively, introducing blockchain networks seems to be an appropriate solution due to their immutability and decentralized nature. Traditional blockchains lack scalability in storing large files, but this can be tackled by storing log data in the interplanetary file system (IPFS). In this paper, we propose a log data storage scheme combined with blockchain networks and the IPFS technique. First, we utilize the IPFS to store extensive file log data and combine blockchain systems to realize decentralized log data storage in a secure manner. Second, to optimize the query efficiency of log data, we propose two improved Merkle tree methods, providing rapid queries of log data with a timestamp as query keywords (only one-third of the BBT in range query time). Finally, our comprehensive experimental results show that the two proposed methods have better log retrieval efficiency.
Keywords:
Blockchain network
Log storage
Interplanetary file system
Merkle tree
Information security
Optimization

Journal

Soft Computing cover
Soft Computing
IF:
2.5
Papers:
1.0W
Citations:
2.1W

Organization

U
University of Hong Kong
Scholars:
4.1W
Papers: 3.9W
Citations: 10.1W
B
beijing university of posts & telecommunications
Scholars:
1.4W
Papers: 1.2W
Citations: 9
N
North China University of Technology
Scholars:
2.0K
Papers: 1.6K
Citations: 962
M
Muroran Institute of Technology
Scholars:
849
Papers: 845
Citations: 439
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