SMDS: Blockchain-Based Sensitive Data Storage for Smart Manufacturing

With the arrival of the digital age and the wide adoption of Internet technologies, the manufacturing industry is gradually entering the era of smart manufacturing. It is of great significance to adopt efficient digital solutions for data management. Organizations tend to choose distributed third-party cloud platforms to store and share data to reduce the costs of building their data platforms. Although distributed cloud storage solutions can solve various problems associated with centralized storage solutions, the collaboration between multiple nodes requires mutual trust. Blockchain technology can help to build trust among different parties. However, the existing consortium blockchain platform does not meet the needs of data privacy and large-scale data storage in smart manufacturing. Therefore, we propose a storage scheme based on the consortium blockchain platform Hyperledger Fabric and the InterPlanetary File System (IPFS). By improving the ledger data structure and designing corresponding smart contract functions, the proposed scheme can store various digital resources on the blockchain platform flexibly and provide fine-grained protection for sensitive data. Several simulation experiments are conducted to analyze the factors affecting the performance. The experiment results show that the space-time overhead of the proposed scheme is acceptable.

Authors

Institutions

Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/bigdatasecurity66063.2025.00019
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

SMDS: Blockchain-Based Sensitive Data Storage for Smart Manufacturing

Zikang Chen, Keke Gai, Liehuang Zhu, Lei Xu et al.
Blockchain Technology Applications and Security
article

SMDS: Blockchain-Based Sensitive Data Storage for Smart Manufacturing

Zikang Chen, Keke Gai, Liehuang Zhu, Lei Xu, Yao Xiao
article en

Abstract

With the arrival of the digital age and the wide adoption of Internet technologies, the manufacturing industry is gradually entering the era of smart manufacturing. It is of great significance to adopt efficient digital solutions for data management. Organizations tend to choose distributed third-party cloud platforms to store and share data to reduce the costs of building their data platforms. Although distributed cloud storage solutions can solve various problems associated with centralized storage solutions, the collaboration between multiple nodes requires mutual trust. Blockchain technology can help to build trust among different parties. However, the existing consortium blockchain platform does not meet the needs of data privacy and large-scale data storage in smart manufacturing. Therefore, we propose a storage scheme based on the consortium blockchain platform Hyperledger Fabric and the InterPlanetary File System (IPFS). By improving the ledger data structure and designing corresponding smart contract functions, the proposed scheme can store various digital resources on the blockchain platform flexibly and provide fine-grained protection for sensitive data. Several simulation experiments are conducted to analyze the factors affecting the performance. The experiment results show that the space-time overhead of the proposed scheme is acceptable.

Beijing Institute of Technology (CN)
National Defense Basic Scientific Research Program of China
Industry, innovation and infrastructure
Openalex Percentile: Top 85%
Blockchain Technology Applications and Security
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.