Titlebook: Artificial Intelligence Security and Privacy; First International Jaideep Vaidya,Moncef Gabbouj,Jin Li Conference proceedings 2024 The Edi

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NAV

輕率的你

周年紀(jì)念日

Expressly

柏樹

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Genome

FedCMK: An Efficient Privacy-Preserving Federated Learning Framework,arning updates the global model by updating the gradient, an attacker may still infer the model update through backward inference, which may lead to privacy leakage problems. In order to enhance the security of federated learning, we propose a solution to this challenge by presenting a multi-key Che

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,An Embedded Cost Learning Framework Based on?Cumulative Gradient Rewards,orks. The GAN has the potential to effectively generate artificial samples that closely resemble the actual sample distribution. The field of steganography utilizing the Generative Adversarial Network (GAN) structure has witnessed a wealth of research with highly successful outcomes. This paper prop

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https://doi.org/10.1007/978-981-99-9785-5Machine learning; Adversarial machine learning; Malware detection and analysis; Privacy-preserving data