New technologies and automation systems have changed the traditional smart grid systems into new and integrated intelligent systems. These new smart systems are adopted for energy efficiency, demand and response, management and control, fault recovery, reliability and quality of services. With various benefits, smart grids have vulnerabilities due to open communication systems, and open infrastructures. Smart grids systems are based on real-time services, where privacy and security id one of the major challenge. In order to address these challenges and deal with security and privacy issues, we proposed a Trust Evaluation Model for Smart Grids (TEMSG) for secure data aggregation in smart grids and smart cities. This model tackles privacy and security issues such as data theft, denial of services, data privacy and inside and outside attacks and malware attacks. Machine learning methods are used to gather trust values and then estimate the imprecise information to secure the data aggregation in smart grids. Experiments are conducted to evaluate and analyze the proposed model in terms of detection rate, trustworthiness, and accuracy.

中文翻译：

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智慧城市智能电网基础设施中安全数据聚合的信任评估模型

新技术和自动化系统已经将传统的智能电网系统变成了新的集成智能系统。这些新的智能系统被用于提高能效，需求和响应，管理和控制，故障恢复，可靠性和服务质量。由于开放的通信系统和开放的基础架构，智能电网具有各种漏洞，存在漏洞。智能电网系统基于实时服务，其中隐私和安全性是主要挑战之一。为了解决这些挑战并解决安全和隐私问题，我们提出了智能电网信任评估模型（TEMSG），用于智能电网和智能城市中的安全数据聚合。此模型可解决隐私和安全问题，例如数据盗窃，拒绝服务，数据隐私以及内部和外部攻击以及恶意软件攻击。机器学习方法用于收集信任值，然后估计不精确的信息以保护智能网格中的数据聚合。进行了实验，以从检测率，可信度和准确性方面评估和分析所提出的模型。