Utilities around the world are reported to invest a total of around 30 billion over the next few years for installation of more than 300 million smart meters, replacing traditional analog meters [1]. By mid-decade, with full country wide deployment, there will be almost 1.3 billion smart meters in place [1]. Collection of fine grained energy usage data by these smart meters provides numerous advantages such as energy savings for customers with use of demand optimization, a billing system of higher accuracy with dynamic pricing programs, bidirectional information exchange ability between end-users for better consumer-operator interaction, and so on. However, all these perks associated with fine grained energy usage data collection threaten the privacy of users. With this technology, customers' personal data such as sleeping cycle, number of occupants, and even type and number of appliances stream into the hands of the utility companies and can be subject to misuse. This research paper addresses privacy violation of consumers' energy usage data collected from smart meters and provides a novel solution for the privacy protection while allowing benefits of energy data analytics. First, we demonstrate the successful application of occupancy detection attacks using a deep neural network method that yields high accuracy results. We then introduce Adversarial Machine Learning Occupancy Detection Avoidance with Blockchain (AMLODA-B) framework as a counter-attack by deploying an algorithm based on the Long Short Term Memory (LSTM) model into the standardized smart metering infrastructure to prevent leakage of consumers personal information. Our privacy-aware approach protects consumers' privacy without compromising the correctness of billing and preserves operational efficiency without use of authoritative intermediaries.

中文翻译：

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利用区块链和对抗性机器学习保护网格用户数据的隐私

据报道，在未来几年中，全球公用事业将投资约300亿欧元，以安装3亿多只智能电表，以取代传统的模拟电表[1]。到十年中，随着在全国范围内的全面部署，将有近13亿个智能电表到位[1]。这些智能电表收集的细粒度能源使用数据具有许多优势，例如通过使用需求优化为客户节省能源，具有动态定价程序的更高准确性的计费系统，最终用户之间的双向信息交换能力，从而使消费者能够更好地操作互动等等。但是，所有与细粒度能源使用数据收集相关的特权都威胁着用户的隐私。借助这项技术，客户的个人数据（例如睡眠周期，人数，甚至设备的类型和数量都流入公用事业公司的手中，并且可能会被滥用。该研究论文解决了从智能电表收集的消费者能源使用数据的隐私侵犯问题，并为隐私保护提供了一种新颖的解决方案，同时又带来了能源数据分析的好处。首先，我们使用深度神经网络方法演示占用检测攻击的成功应用，该方法可产生高精度结果。然后，我们通过将基于长期短期记忆（LSTM）模型的算法部署到标准化的智能计量基础架构中，以防止消费者的个人信息泄漏，来引入对抗性的区块链机器学习占位检测避免（AMLODA-B）框架。 。