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The existing static ABAC(Attribute-Based Access Control) model cannot fully meet the increasingly complex, dynamic and scalable demands of the power grid. At the same time, its versatility and flexibility bring high costs. Additionally, the increasing complexity of organizational systems and the need for federated access to their resources make implementing and managing access control more challenging. This paper proposes a fine-grained dynamic access control method based on Kformer to automate authorization management tasks. We use Kformer, which filters and integrates external knowledge through feed-forward layers in Transformer. Then, we use BERT(Bidirectional Encoder Representations from Transformer) to perform feature extraction on the input fused text, extract the implied attribute-authority relationship from the log records and external documents, and finally, perform sequence modeling on the extracted attribute features and input the obtained results. The final authorization result is obtained by classification through the softmax function in the final fully connected layer. The authorization management of the user’s request to the object is dynamically completed. Finally, using the access data of the grid information system to evaluate the method proposed by us, the experimental results show that the model can continuously monitor the access behavior of users inside the grid information system, change the access rights of entities and adjust the policy in real-time, and carry out dynamic access authorization. At the same time, the accuracy of the generated access control policy can reach 87.73%.

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.

The edge perception layer of electric Internet of Things (eIoT) involves a large amount of user’s data, including identity, location and other sensitive data, facing the risk of privacy disclosure. To solve this problem, we first established an eIoT model based on edge IoT agent, and propose a dynamic privacy preserving scheme using Multi-identity-based Fully Homomorphic Encryption (MIBFHE). Different from the exiting schemes, the system is no longer limited to the same secret key. Each user generating their own secret key on the basis of identity, and key management, revocation are much easier to achieve. In addition, our scheme is dynamically faulttolerant, and it can still be normal to calculate even if the device is added or failed. The experimental results verify the effectiveness and advantage in the cost of computational and communication compared to the existing schemes.

Smart grid is a highly integrated power system that combines modern advanced information and communication, sensor measurement and automatic control technology, including a large number of smart devices, with reliability, flexibility, safety and self-healing advantages a great service. However, devices such as smart meter in the network face the issue of disclosing user privacy when collecting, processing, and transmitting large amounts of data. To solve this problem, this paper puts forward a scheme based on improved group signature and homomorphic encryption, using improved group signature to determine the public and private key of smart meter and the signature of electricity data, encrypting and aggregating the electricity data with homomorphic encryption. Finally, in the transmission of data, the electricity data is always in the form of ciphertext, and the group signature scheme with forwarding security can avoid the harm caused by the disclosure of the private key, thus playing a role in privacy-preserving.

The false data injection attack can tamper with the measurement information collected by the Supervisory Control and Data Acquisition, threat the security of state estimation. Therefore, the analysing methods and detection methods of false data injection attacks have important theoretical and practical significance for ensuring the safe operation of smart grids. This study uses the invertible automatic encoder to reduce the dimension of the original data and uses the long-short-term memory to detect false data injection attacks. This method overcomes the shortcomings of shallow algorithm and traditional machine learning algorithm for power big data training and avoiding the problems of gradient explosion and gradient disappearing during training. Finally, the authors perform a large number of experiments in the IEEE 118-node test system and the 300-node test system and verify the effectiveness of the proposed method.