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In smart grids, two-way communication between end-users and the grid allows frequent data exchange, which on one hand enhances users' experience, while on the other hand increase security and privacy risks. In this paper, we propose an efficient system to address security and privacy problems, in contrast to the data aggregation schemes with high cryptographic overheads. In the proposed system, users are grouped into local communities and trust-based blockchains are formed in each community to manage smart grid transactions, such as reporting aggregated meter reading, in a light-weight fashion. We show that the proposed system can meet the key security objectives with a detailed analysis. Also, experiments demonstrated that the proposed system is efficient and can provide satisfactory user experience, and the trust value design can easily distinguish benign users and bad actors. 

Smart grids can be vulnerable to attacks and accidents, and any initial failures in smart grids can grow to a large blackout because of cascading failure. Because of the importance of smart grids in modern society, it is crucial to protect them against cascading failures. Simulation of cascading failures can help identify the most vulnerable transmission lines and guide prioritization in protection planning, hence, it is an effective approach to protect smart grids from cascading failures. However, due to the enormous number of ways that the smart grids may fail initially, it is infeasible to simulate cascading failures at a large scale nor identify the most vulnerable lines efficiently. In this paper, we aim at 1) developing a method to run cascading failure simulations at scale and 2) building simplified, diffusion based cascading failure models to support efficient and theoretically bounded identification of most vulnerable lines. The goals are achieved by first constructing a novel connection between cascading failures and natural languages, and then adapting the powerful transformer model in NLP to learn from cascading failure data. Our trained transformer models have good accuracy in predicting the total number of failed lines in a cascade and identifying the most vulnerable lines. We also constructed independent cascade (IC) diffusion models based on the attention matrices of the transformer models, to support efficient vulnerability analysis with performance bounds.