Privacy Leakage

Privacy leakage in machine learning, particularly concerning large language models (LLMs), focuses on identifying and mitigating the unintentional release of sensitive information from training data or model outputs. Current research emphasizes developing methods to trace leaked information back to its source, employing techniques like influence functions and differential privacy, and analyzing privacy risks within collaborative learning frameworks such as federated learning. This work is crucial for ensuring responsible AI development and deployment, balancing the utility of powerful models with the fundamental right to privacy. The ultimate goal is to create robust mechanisms that prevent privacy violations while maintaining model performance.