Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

中文翻译：

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IFed：电力物联网中局部差异隐私的新型联邦学习框架

如今，无线传感器网络技术越来越流行，广泛应用于物联网。尤其是电力物联网是物联网系统中一个重要且发展迅速的部分，它得益于无线传感器网络的应用实现了细粒度的信息采集。同时，隐私风险也逐渐暴露，这是电力消费者普遍关注的问题。特别是非侵入式负载监控是一种仅从能耗数据中恢复电器状态的技术，它使攻击者能够推断居民的行为隐私。毫无疑问，对于电力客户来说，应用本地差分隐私来实现本地环境中的隐私保护比集中式方法更值得信赖。虽然传统的局部差分隐私混淆机制难以控制风险，实现隐私与效用的权衡，但现有的一些基于人工智能的混淆机制，称为高级混淆机制，可以实现。然而，训练机器学习模型所消耗的大量计算资源对于大多数电力物联网终端来说是无法承受的。在本文中，为了解决这个问题，提出了IFed——一种新颖的联邦学习框架，让通常计算资源充足的电力供应商帮助电力物联网用户。首先，提出了优化的框架，其中结合了本地差异隐私、数据效用和资源消耗之间的权衡。同时，注意到并解决了电力供应商和客户之间机器学习模型传输的以下隐私保护问题。最后，根据不同级别的隐私需求对用户进行分类，为敏感用户提供更强的隐私保障。正式的局部差分隐私分析和实验表明，IFed 可以满足电力物联网用户的隐私要求。