IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i3d10.1007_s10845-020-01682-y.html
   My bibliography  Save this article

A post-quantum secure communication system for cloud manufacturing safety

Author

Listed:
  • Haibo Yi

    (Shenzhen Polytechnic
    City University of Seattle)

Abstract

In recent years, as one of the new advanced manufacturing modes, cloud manufacturing has been received wide attentions around the world. The technology of cloud manufacturing intergrades the services-oriented techniques as well as manufacturing processes based on cloud computing. With the aid of the cloud computing platforms, the manufacturing services are provided in manufacturing clouds. However, one of the key challenges of cloud manufacturing is the security and safety of information transmission. Traditional network security architectures are based on RSA and elliptic curve cryptographic systems, which is claimed to be broken on quantum computers. We exploit the countermeasures of post-quantum algorithms to protect cloud manufacturing against quantum computer attacks. We propose a post-quantum secure scheme for cloud manufacturing. First, in order to retain confidentiality in cloud manufacturing, we propose a post-quantum asymmetric-key encryption scheme to encrypt the message with the generated session key. Second, in order to retain authentication security in cloud manufacturing, we propose a post-quantum public-key signature generation scheme. Third, based on the encryption scheme and signature generation scheme, we propose a post-quantum secure communication system for cloud manufacturing. We implement our design on cloud-based environment and the comparison with related designs show that our design is suitable for protecting communication in cloud manufacturing. Besides, the post-quantum secure communication system can be extended to other applications of intelligent manufacturing.

Suggested Citation

  • Haibo Yi, 2021. "A post-quantum secure communication system for cloud manufacturing safety," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 679-688, March.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01682-y
    DOI: 10.1007/s10845-020-01682-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01682-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-020-01682-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Minghai Yuan & Hongyan Yu & Jinting Huang & Aimin Ji, 2019. "Reconfigurable assembly line balancing for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2391-2405, August.
    2. Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    3. Jinjiang Wang & Laibin Zhang & Lixiang Duan & Robert X. Gao, 2017. "A new paradigm of cloud-based predictive maintenance for intelligent manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1125-1137, June.
    4. Malik Khalfallah & Nicolas Figay & Catarina Ferreira Da Silva & Parisa Ghodous, 2016. "A cloud-based platform to ensure interoperability in aerospace industry," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 119-129, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    2. Delin Zeng & Jingbo Hu & Taohua Ouyang, 2017. "Managing Innovation Paradox in the Sustainable Innovation Ecosystem: A Case Study of Ambidextrous Capability in a Focal Firm," Sustainability, MDPI, vol. 9(11), pages 1-15, November.
    3. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    4. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    5. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    6. Neeraj Gupta & Saurabh Gupta & Mahdi Khosravy & Nilanjan Dey & Nisheeth Joshi & Rubén González Crespo & Nilesh Patel, 2021. "RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1117-1128, April.
    7. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.
    9. Yu Feng & Biqing Huang, 2020. "Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1649-1660, October.
    10. Rui Liu, 2023. "An edge-based algorithm for tool wear monitoring in repetitive milling processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2333-2343, June.
    11. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    12. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.
    13. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    14. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01682-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.