Box Membership Inference Attack

Box membership inference attacks (MIAs) aim to determine if a specific data point was used to train a machine learning model, posing a significant privacy risk. Current research focuses on improving MIA effectiveness across various model architectures, including deep neural networks, large language models, and diffusion models, exploring both black-box (limited model access) and white-box (full model access) attack scenarios. These attacks leverage techniques like analyzing loss curvature, exploiting model memorization, and employing knowledge distillation to enhance inference accuracy, highlighting the urgent need for robust privacy-preserving machine learning techniques. The impact of these attacks extends to various applications, from image classification and natural language processing to medical diagnosis, underscoring the importance of developing effective defenses.