Elsevier

High-Confidence Computing Template

Write in a clean editor, then format for High-Confidence Computing in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.

About the High-Confidence Computing format

High-Confidence Computing is a peer-reviewed journal published by Elsevier, covering Blockchain Technology Applications and Security, Privacy-Preserving Technologies in Data, Cryptography and Data Security.

PublisherElsevier
Reference styleNumbered (Elsevier)
Numbered — [1], [2] in the text
[1] A. Smith, B. Jones, C. Lee, A representative article title, High-Confidence Computing 12 (2023) 45–58.

Formats any DOI in High-Confidence Computing style. No sign-up.

Publishes research inBlockchain Technology Applications and Security Privacy-Preserving Technologies in Data Cryptography and Data Security IoT and Edge/Fog Computing Network Security and Intrusion Detection
ISSN2667-2952
Citation impact (2-yr)11.83
h-index24
i10-index75
Total citations3,391
Article processing charge$1,500
Open accessYes
Top institutions publishing hereShandong University of Science and Technology
Journal websitewww.journals.elsevier.com
You getA submission-ready PDF and the editable LaTeX source — ready to submit.

Papers published in High-Confidence Computing per year

25
2021
28
2022
54
2023
61
2024
57
2025

Citation impact of High-Confidence Computing by publication year

722
2021
392
2022
598
2023
1.4K
2024
179
2025

Citations each year’s papers have accumulated so far — the most recent years are still building up.

Most-cited papers in High-Confidence Computing

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly

Yifan Yao, Jinhao Duan, Kaidi Xu et al. · 29 Feb 2024

Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community,…

A survey of federated learning for edge computing: Research problems and solutions

Qi Xia, Winson Ye, Zeyi Tao et al. · 23 Mar 2021

Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. It can provide better data privacy because training data are not transmitted to a central server. Federated learning is well suited for edge computing applications and can…

A survey on blockchain systems: Attacks, defenses, and privacy preservation

Yourong Chen, Hao Chen, Yang Zhang et al. · 17 Dec 2021

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network…

When blockchain meets smart grids: A comprehensive survey

Yihao Guo, Zhiguo Wan, Xiuzhen Cheng · 27 Mar 2022

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy…

A trustless architecture of blockchain-enabled metaverse

Minghui Xu, Yihao Guo, Qin Hu et al. · 29 Dec 2022

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as…

High-Confidence Computing template — frequently asked questions

How do I write a paper in the High-Confidence Computing format?
In DocuGuru you write your manuscript in a normal editor — no LaTeX setup required — and select the High-Confidence Computing template. When you export, DocuGuru compiles the paper into the official Elsevier format and hands you a submission-ready PDF along with the editable LaTeX source.
What reference style does High-Confidence Computing use?
High-Confidence Computing uses Numbered (Elsevier) references, shown as numbered [1], [2] markers in the text. DocuGuru formats every in-text citation and the reference list in this exact style automatically. A reference appears like this: [1] A. Smith, B. Jones, C. Lee, A representative article title, High-Confidence Computing 12 (2023) 45–58.
Do I need to know LaTeX to submit to High-Confidence Computing?
No. DocuGuru generates the elsarticle LaTeX class and compiles the PDF for you in the background, so you get a Elsevier-ready High-Confidence Computing document without writing any LaTeX. If you do want it, the LaTeX source is included in the export.
Can I import an existing draft into the High-Confidence Computing template?
Yes. Paste or upload your current manuscript — Word, LaTeX, Markdown, or plain text — and DocuGuru reflows it into the High-Confidence Computing format with correct headings, figures, tables, and numbered citations.
Who publishes High-Confidence Computing?
High-Confidence Computing is a multidisciplinary journal published by Elsevier. DocuGuru's High-Confidence Computing template matches Elsevier's official submission format.
Can I export a submission-ready High-Confidence Computing PDF?
Yes — DocuGuru produces a PDF built with the official High-Confidence Computing template (the elsarticle class) that is ready to submit to Elsevier, together with the matching LaTeX source files.
How much does the High-Confidence Computing template cost?
You can start writing in the High-Confidence Computing template for free. Exporting the final submission-ready High-Confidence Computing PDF and LaTeX source is part of DocuGuru's paid plans — see the app for current pricing.
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