# Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions

arXiv: https://arxiv.org/abs/2412.06113 · HTML: https://mlsanity.com/paper/2412.06113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [On Protecting the Data Privacy of Large Language Models (LLMs): A Survey](https://mlsanity.com/paper/2403.05156/) (2024)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners](https://mlsanity.com/paper/2310.02469/) (2023)
- [Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework](https://mlsanity.com/paper/2310.12523/) (2023)
- [State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey](https://mlsanity.com/paper/2404.16847/) (2024)
- [Local Privacy-preserving Mechanisms and Applications in Machine Learning](https://mlsanity.com/paper/2401.13692/) (2024)

## Built on by (most cited first, 5 of 5)

- [ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs](https://mlsanity.com/paper/2506.01386/) (2025)
- [ThinkTank: A Framework for Generalizing Domain-Specific AI Agent Systems into Universal Collaborative Intelligence Platforms](https://mlsanity.com/paper/2506.02931/) (2025)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)
- [Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models](https://mlsanity.com/paper/2606.03399/) (2026)
- [SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs](https://mlsanity.com/paper/2609.36879/) (2026)

## Abstract (arXiv)

The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This survey comprehensively explores the landscape of privacy-preserving mechanisms tailored for LLMs, including differential privacy, federated learning, cryptographic protocols, and trusted execution environments. We examine their efficacy in addressing key privacy challenges, such as membership inference and model inversion attacks, while balancing trade-offs between privacy and model utility. Furthermore, we analyze privacy-preserving applications of LLMs in privacy-sensitive domains, highlighting successful implementations and inherent limitations. Finally, this survey identifies emerging research directions, emphasizing the need for novel frameworks that integrate privacy by design into the lifecycle of LLMs. By synthesizing state-of-the-art approaches and future trends, this paper provides a foundation for developing robust, privacy-preserving large language models that safeguard sensitive information without compromising performance.