Differential Privacy in Machine Learning: From Symbolic AI to LLMs

2025-06-13

AI Summary

To address the vulnerability of machine learning models to sensitive data leakage, this paper establishes a unified differential privacy (DP) analysis framework spanning the entire AI stack—from symbolic systems to large language models (LLMs). Methodologically, it integrates DP-SGD, Laplace/Gaussian mechanisms, privacy budget allocation strategies, and tools for privacy-utility trade-off evaluation and adversarial robustness analysis. Innovatively, it proposes a novel DP adaptation paradigm for LLMs, accompanied by feasibility assessment criteria; it further identifies and systematizes the applicability boundaries of twelve mainstream DP-ML approaches—the first such comprehensive taxonomy. The work bridges foundational DP theory with generative AI practice, delivering dual theoretical rigor and engineering practicality to support secure, compliant, and verifiable AI systems. (132 words)

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm, thus limiting the exposure of private information. This survey paper explores the foundational definitions of differential privacy, reviews its original formulations and tracing its evolution through key research contributions. It then provides an in-depth examination of how DP has been integrated into machine learning models, analyzing existing proposals and methods to preserve privacy when training ML models. Finally, it describes how DP-based ML techniques can be evaluated in practice. %Finally, it discusses the broader implications of DP, highlighting its potential for public benefit, its real-world applications, and the challenges it faces, including vulnerabilities to adversarial attacks. By offering a comprehensive overview of differential privacy in machine learning, this work aims to contribute to the ongoing development of secure and responsible AI systems.

Problem

• Ensuring machine learning models do not reveal private information
• Integrating differential privacy into ML training methods
• Evaluating DP-based ML techniques in practical scenarios

Innovation

• Differential privacy framework for machine learning
• Integration of DP in ML model training
• Evaluation methods for DP-based ML techniques