Privacy-Preserving Information Extraction for Ethical Case Studies in Machine Learning Using ChatGLM-LtMP
Year: 2025
Type: article
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.
Source: Electronics
Authors: Xindan Gao, Xinyi Ba, Jian Xing, Ying Liu
Institution: Northeast Forestry University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G462860947
OpenAlex ID: W4408938386