Critical nodes play a pivotal role in ensuring the security and reliability of power grids, given that attacking them can lead to widespread power outages. Consequently, identifying critical nodes within the power grid holds immense significance. However, prior research either focuses on a single node metric or subjectively assigns weights to multiply metrics, resulting in inaccurate ranking lists. To overcome these limitations, this paper introduces the CGA algorithm to identify critical nodes in the power grid using deep learning methods. Specifically, CGA employs the contraction algorithm to establish feature matrices and utilizes the Susceptible-Infectious-Recovered (SIR) model to generate labels for nodes. Subsequently, CGA leverages the Convolutional Neural Network (CNN) to encode node information, effectively reducing computational complexity, then applies the Graph Neural Network (GNN) with an attention mechanism to learn node hidden representation. In addition, this paper employs two evaluation metrics to assess the effectiveness and distinguishability of CGA, including Kendall’s tau correlation coefficient and monotonicity index. Through conducting extensive experiments on the three datasets, simulation results demonstrate that CGA performs better than the six baseline algorithms, in which the effectiveness of identifying critical nodes is improved and the distinguishability of the ranking list is enhanced.
KEYWORDS: Education and training, Data privacy, Data modeling, Gallium nitride, Power supplies, Mathematical optimization, Design, Telecommunications, Telecommunication networks, Performance modeling
With the rapid proliferation of smart grid technologies, a large amount of fine-grained power data records has been collected and stored by different parties (e.g., the power supply bureau). Pooling together the records held by the parties makes mining the data value, and therefore promises enhanced energy management and efficiency. Despite the benefits of sharing these data, it also raises concerns about data privacy and security. To this end, we present a novel approach for privacy-preserving cross-party power data sharing approach in light of the Generative Adversarial Network (named PowerGAN), which enables the involved parties to construct a shared dataset without compromising the privacy of these parties. In PowerGAN, a centralized curator is assigned a generator, while each party possesses a discriminator. The key idea of PowerGAN is to let data holders jointly train the generator held by the centralized server. In addition, to prevent the curator from inferring sensitive data about the parties, we designed a privacy preserving RMSProp (Root Mean Square Propagation) optimizer. Furthermore, we design a dynamic noise perturbation method, which dynamically tunes the noise to further promote the utility of the final shared data. Through comprehensive privacy analysis, we show that our PowerGAN approach provides strict privacy protection. Evaluations of real-world datasets show the effectiveness of PowerGAN in addressing the privacy concerns associated with multi-party power data sharing.
Federated learning is an effective method to solve the problem of data silos, but adversarial attacks launched based on adversarial samples pose a great threat to the security of federated learning models. This makes the application and promotion of federated learning somewhat affected. Therefore, this paper verifies the performance of a defense method for adversarial attacks in federated learning scenario, which is proposed in the traditional machine learning. The method defends adversarial attacks mainly by performing an input transformation before feeding images to the model. We conducted experiments on the EMnist dataset, and the experimental results show that this defense strategy can also improve the robustness of federation learning under different adversarial attacks.
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