Evidence map›Paper›PMID 37631574›Full record

ArticleSensors (Basel, Switzerland)2023

Blockchain-Based Secure Storage and Access Control Scheme for Supply Chain Ecological Business Data: A Case Study of the Automotive Industry.

Songjiang Li, Tao Zhou, Huamin Yang, Peng Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Songjiang LiCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Tao ZhouCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0009-0002-8951-4616
Huamin YangCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.ORCID 0000-0002-4141-4715
Peng WangCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Funding

the Jilin Provincial Science and Technology Innovation Center for Network Database Application YDZJ202302CXJD027The National Key Research and Development Program of China 2021YFB1714400
6 · The paper itself

Abstract

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.

Indexed as

attribute-based access controlauditabilityautomotive supply chainblockchainHyperledger Fabricsecure storagesmart contract

Identifiers

PMID37631574
PMCPMC10457888

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.