IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3974-d250653.html
   My bibliography  Save this article

BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

Author

Listed:
  • Shailendra Rathore

    (Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Yi Pan

    (Department of Computer Science, Georgia State University, Atlanta, GA 30302-5060, USA)

  • Jong Hyuk Park

    (Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

Abstract

The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.

Suggested Citation

  • Shailendra Rathore & Yi Pan & Jong Hyuk Park, 2019. "BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3974-:d:250653
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3974/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/14/3974/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    2. Florentina Magda Enescu & Nicu Bizon & Adrian Onu & Maria Simona Răboacă & Phatiphat Thounthong & Alin Gheorghita Mazare & Gheorghe Șerban, 2020. "Implementing Blockchain Technology in Irrigation Systems That Integrate Photovoltaic Energy Generation Systems," Sustainability, MDPI, vol. 12(4), pages 1-30, February.

    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:gam:jsusta:v:11:y:2019:i:14:p:3974-:d:250653. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.