【Author】
Shen, Meng; Wang, Huan; Zhang, Bin; Zhu, Liehuang; Xu, Ke; Li, Qi; Du, Xiaojiang
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】Federated learning (FL) serves as an enabling technology for intelligent edge computing, where high-quality machine learning (ML) models are collaboratively trained over large amounts of data generated by various Internet of Things devices while preserving data privacy. To further provide data confidentiality, computation auditability, and participant incentives, the blockchain framework has been incorporated into FL. However, it is an open question whether the model updates from participants in blockchain-assisted FL can disclose properties of the private data the participants are unintended to share. In this article, we propose a novel property inference attack that exploits the unintended property leakage in blockchain-assisted FL for intelligent edge computing. More specifically, we present an active attack to learn the property leakage from model updates of participants and to identify a set of participants with a certain property. We also design a dynamic participant selection strategy tailored to the setting of large-scale FL, which accelerates the selection process of target participants and improves attack accuracy. We evaluate the proposed attack through extensive experiments with publicly available data sets. The experimental results demonstrate that the proposed attack is effective and efficient in inferring various properties of training data, while maintaining the high quality of the main tasks in FL.
【Keywords】Servers; Training; Computational modeling; Collaborative work; Data models; Training data; Blockchain; Blockchain; edge computing; federated learning (FL); Internet of Things (IoT); property inference
【标题】利用区块链辅助联邦学习中的意外属性泄漏进行智能边缘计算
【摘要】联邦学习 (FL) 是智能边缘计算的支持技术，其中高质量的机器学习 (ML) 模型在各种物联网设备生成的大量数据上进行协作训练，同时保护数据隐私。为了进一步提供数据机密性、计算可审计性和参与者激励，区块链框架已被纳入 FL。然而，区块链辅助 FL 参与者的模型更新是否可以披露参与者无意共享的私有数据的属性，这是一个悬而未决的问题。在本文中，我们提出了一种新颖的属性推理攻击，该攻击利用区块链辅助 FL 中的意外属性泄漏进行智能边缘计算。更具体地说，我们提出了一种主动攻击，以从参与者的模型更新中学习属性泄漏，并识别一组具有特定属性的参与者。我们还设计了针对大规模FL设置的动态参与者选择策略，加快了目标参与者的选择过程，提高了攻击的准确性。我们通过对公开可用数据集的广泛实验来评估提议的攻击。实验结果表明，所提出的攻击在推断训练数据的各种属性方面是有效且高效的，同时保持了 FL 中主要任务的高质量。
【关键词】服务器；训练;计算建模；协作工作；数据模型；训练数据；区块链；区块链；边缘计算；联邦学习（FL）；物联网（IoT）；属性推断
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