Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing
Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing
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DOI：
10.1109/jbhi.2022.3157725
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发表时间：
2023-02-01
影响因子：
7.7
通讯作者：
Karuppiah, Marimuthu
中科院分区：
文献类型：
--
作者：
Wang, Ruijin;Lai, Jinshan;Karuppiah, Marimuthu
边缘智能计算广泛应用于医疗物联网等领域，具有数据处理效率高、实时性强、网络时延低等优点。然而，也存在许多问题，包括隐私泄露、计算能力有限以及调度和协调问题。联合学习可以极大地提高训练效率。然而，由于医疗数据的敏感性质，上述将患者数据传输到服务器的方法可能会产生严重的安全和隐私问题。为此，本文提出了一种边缘计算环境下联合学习的隐私保护方案(PPFLEC)。首先，基于秘密共享的随机掩码方案，提出了一种基于共享秘密和加权掩码的轻量级隐私保护协议。它比同态加密更准确、更高效。它不仅可以在不损失模型准确性的情况下保护梯度隐私，而且可以抵抗设备掉落和设备之间的合谋攻击。其次，我们设计了一种基于数字签名和哈希函数的算法，实现了消息的完整性和一致性，并抵抗了重放攻击。最后，我们提出了一种周期平均训练策略，并与差分私密性进行了比较，证明了我们的方案在效率上比尊重私密性快40%。同时，与联邦学习相比，在保证安全性的情况下，可以达到相同的效率。因此，我们的方案可以在不稳定的边缘计算环境中很好地工作，例如智能医疗。
Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT), which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force, as well as scheduling and coordination issues. Federated learning can greatly improves training efficiency. However, due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than, homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the integrity and consistency of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40% faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.