Privacy-Preserving Information Extraction for Ethical Case Studies in Machine Learning Using ChatGLM-LtMP

Abstract

Ensuring privacy protection in machine learning is crucial for handling sensitive information, particularly in ethical case studies within computer engineering. Traditional information extraction methods often expose private data to risks such as membership inference and reconstruction attacks, compromising confidentiality. To address these concerns, we propose ChatGLM-LtMP, a privacy-preserving information extraction framework that integrates Least-to-Most Prompting and P-Tuning v2 for structured and secure data retrieval. By employing controlled prompting mechanisms, our approach minimizes data exposure while maintaining high accuracy (93.71%), outperforming baseline models. Additionally, we construct a knowledge graph using the Neo4j 4.4 database and integrate LangChain 0.2 for case-based intelligent question answering. This framework enables secure and interpretable extraction of ethical case data, making it suitable for applications in sensitive machine learning scenarios. The proposed method advances information extraction, safeguarding sensitive ethical cases from potential attacks in automated learning environments.