- [2024/06] Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
- [2024/05] Learnable Privacy Neurons Localization in Language Models
- [2024/05] Information Leakage from Embedding in Large Language Models
- [2024/05] Air Gap: Protecting Privacy-Conscious Conversational Agents
- [2024/04] Can LLMs get help from other LLMs without revealing private information?
- [2024/03] Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
- [2024/03] PreCurious: How Innocent Pre-Trained Language Models Turn into Privacy Traps
- [2024/03] Visual Privacy Auditing with Diffusion Models
- [2024/03] Analysis of Privacy Leakage in Federated Large Language Models
- [2024/03] CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following
- [2024/02] The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
- [2024/01] Excuse me, sir? Your language model is leaking (information)
- [2023/10] Last One Standing: A Comparative Analysis of Security and Privacy of Soft Prompt Tuning, LoRA, and In-Context Learning
- [2023/09] Beyond Memorization: Violating Privacy via Inference with Large Language Models
- [2023/09] Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
- [2023/09] Privacy Side Channels in Machine Learning Systems
- [2023/07] ProPILE: Probing Privacy Leakage in Large Language Models
- [2023/05] ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review