Reconstruction Attack

Reconstruction attacks exploit trained machine learning models to recover sensitive information from their training data, aiming to understand and quantify the privacy risks inherent in model deployment. Current research focuses on developing increasingly sophisticated attack methods, often leveraging neural networks, generative models (like StyleGAN and diffusion models), and optimization techniques to reconstruct data from model parameters, gradients, or even model outputs. These attacks highlight significant vulnerabilities in various machine learning applications, from federated learning and biometric systems to image recognition and language models, prompting the development of robust privacy-preserving techniques and improved privacy auditing methods.