Comments on 'Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT'

Zhiyuan Sui, Yujiao Sun, Jianming Zhu, Fu Chen*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

Federated learning has been used to collaboratively train a decentralized model without sharing confidential model records. However, many security risks are involved in this regard, and scholars have conducted numerous studies on related topics. Fan et al. proposed a scheme to achieve model unforgeability and confidentiality in blockchained federated learning (lightweight privacy blockchained federated-learning (LPBFL) scheme) in the Internet of Things. Our research demonstrates that their scheme is insecure in terms of signature design and that their theorem is invalid. We present an effective attack on their signature algorithm and create a new signature method and a formal security model to provide security guarantee against the mentioned attack. Extensive simulations demonstrate that our signature algorithm does not harm the highly efficient LPBFL scheme.

源语言英语
页(从-至)15043-15046
页数4
期刊IEEE Internet of Things Journal
11
8
DOI
出版状态已出版 - 15 4月 2024

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Sui, Z., Sun, Y., Zhu, J., & Chen, F. (2024). Comments on 'Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT'. IEEE Internet of Things Journal, 11(8), 15043-15046. https://doi.org/10.1109/JIOT.2024.3358302