【Author】
Jia, Bin; Zhang, Xiaosong; Liu, Jiewen; Zhang, Yang; Huang, Ke; Liang, Yongquan
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.
【Keywords】Industrial Internet of Things; Collaborative work; Data models; Computational modeling; Distributed databases; Security; Blockchain; Blockchain; differential privacy; federated learning; homomorphic encryption; privacy protection
【标题】IIoT中具有差分隐私和同态加密的区块链联邦学习数据保护聚合方案
【摘要】随着物联网中不同工业设备产生的数据量快速增长，保护数据共享中的敏感数据和私有数据变得至关重要。目前已经出现了面向数据安全的联邦学习，它可以通过互不信任的互联网模型共享来解决数据共享的安全问题。但是，黑客仍然针对联邦学习中的安全漏洞（例如模型提取攻击和模型反向攻击）发起攻击。在本文中，针对上述问题，我们首先设计了区块链联邦学习在工业物联网（IIoT）中的应用模型，并基于上述模型制定了我们的数据保护聚合方案。然后，我们给出了基于差分隐私和同态加密的分布式K-means聚类，差分隐私的分布式随机森林和同态加密方法的分布式AdaBoost，实现了数据共享和模型共享的多重数据保护。最后，我们将这些方法与区块链和联邦学习相结合，并提供完整的安全分析。大量的实验结果表明，我们的聚合方案和工作机制在所选指标上具有更好的性能。
【关键词】工业物联网；协作工作；数据模型；计算建模；分布式数据库；安全;区块链；区块链；差异隐私；联邦学习；同态加密；隐私保护
评论