Inference Privacy

Inference privacy focuses on protecting sensitive information embedded within data used for machine learning, aiming to prevent adversaries from inferring private details about individuals or groups from model outputs or training processes. Current research emphasizes developing and evaluating privacy-preserving techniques, including differential privacy, data masking, and secure multi-party computation, often within the context of specific model architectures like large language models and deep neural networks. This field is crucial for responsible AI development, enabling the use of sensitive data in machine learning applications while mitigating privacy risks and fostering trust in AI systems.