With electric vehicle's charging information, the utilities can increase the efficiency and reliability of Vehicle-to-Grid (V2G) while the electric vehicle consumers can better manage their energy consumption and costs. However, since charging information are commonly transmitted via public network or wireless link, their communications lack trusted third party for identity authentication and key distribution, and are constantly exposed to traffic analysis attacks. In this paper, we propose a secure and anonymous communication scheme for delivering charging information in V2G. Different from previous works, the scheme in this paper creatively incorporates identity authentication into key distribution without trusted third party, which improves the security of communication in public networks. Moreover, the proposed scheme is more resistant to traffic analysis since it preserves the anonymity of charging information by splitting and forwarding them pseudo-randomly. As the performance analysis reveals, our scheme is able to provide better anonymity without the support of trusted third party, and what's more, it can achieve high cryptography capability as traditional communication schemes do. Therefore, it is more practical in real-world V2G.
The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacysensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements. We decompose the named entity recognition (NER) model FLAT (Chinese NER Using Flat-Lattice Transformer) in each platform into a global part and a local part. The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data. The global part is learned across different operation platforms to capture the shared NER knowledge. Its local gradients from different platforms are aggregated to update the global model, which is further delivered to each platform to update their global part. Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.
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