Privacy-Preserving Machine Learning in Large Language Models (LLMs)

Dear Colleagues,

In recent years, the field of artificial intelligence has witnessed significant advancements, notably in the development of Large Language Models (LLMs) like GPT, BERT, and others. These models, powered by extensive datasets and complex neural network architectures, have shown remarkable capabilities in generating human-like text, understanding context, and even performing sophisticated reasoning tasks. However, the rapid adoption and integration of LLMs across various sectors raise substantial concerns regarding data privacy and security.

The proposed Special Issue will focus on “Privacy-Preserving Machine Learning in Large Language Models (LLMs)”, aiming to spotlight innovative research, methodologies, and technologies that ensure privacy and security in the training and application phases of LLMs. It will cover theoretical advancements, practical implementations, and regulatory considerations that address how data used in LLMs can be protected against unauthorized access and misuse.

This Special Issue welcomes original research articles, review papers, case studies, and short communications on topics including, but not limited to, the following:

• Techniques for anonymizing data used in training LLMs;
• Federated learning approaches for decentralized model training;
• Differential privacy techniques and their application in LLMs;
• Secure multi-party computation (SMPC) solutions for LLMs;
• Homomorphic encryption methods for privacy-preserving computations in LLMs;
• Assessment of privacy risks and vulnerabilities in existing LLM frameworks;
• Policy and regulatory frameworks for privacy in AI and machine learning;
• Case studies on the implementation of privacy-preserving mechanisms in LLMs;
• Ethical implications of data privacy in LLMs;
• Mathematic foundations for explainable LLMs;
• Machine Unlearning and its application in LLMs.

Dr. Zuobin Ying
Prof. Dr. Jinbo Xiong
Guest Editors

Manuscript Submission Information

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• privacy preserving
• large language models
• machine unlearning