Domain Inference Attack

Domain inference attacks aim to extract sensitive information about the training data or intended application of a machine learning model, even with limited knowledge of its purpose. Current research focuses on developing sophisticated attacks, such as those employing generative adversarial networks (GANs) or adaptive algorithms to infer relevant training data subsets, even without explicit domain knowledge. These attacks highlight vulnerabilities in deployed models, particularly in sensitive sectors like healthcare and security, underscoring the need for robust model defenses and privacy-preserving techniques. The impact of this research is significant, driving the development of more secure and privacy-aware machine learning systems.