October 12, 2021 preprint Open access Large Language Models Can Be Strong Differentially Private Learners arXiv (Cornell University) DOI: 10.48550/arxiv.2110.05679 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 3 stored of 72 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review 2025 High-Confidence Computing article Computer Science Privacy-Preserving Technologies in Data Open access cited by 1 patent 82 citations The Limits of Word Level Differential Privacy 2022 Findings of the Association for Computational Linguistics: NAACL conference-paper Computer Science Privacy-Preserving Technologies in Data Open access 20 citations Differentially Private Language Models for Secure Data Sharing 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) conference-paper Computer Science Privacy-Preserving Technologies in Data Open access 16 citations 3 results References 0