🌟⬛️Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation |
(ICLR, 2024) |
|
Classification, Information Extraction |
|
🌟⬛️DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer |
(ICLR, 2024) |
|
Sentiment Classification |
|
🌟⬛️Privacy-Preserving In-Context Learning For Large Language Models |
(ICLR, 2024) |
|
Classification, Document Q&A, Dialog Summarization |
|
⬛️On the Privacy Risk of In-context Learning |
(TrustNLP, 2024)) |
|
Classification, Generation |
MIA |
⬛️A Customized Text Sanitization Mechanism with Differential Privacy |
(ACL, 2023) |
|
Classification, Generation |
|
🌟⬛️⬜️Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models |
(NeurIPS, 2023) |
|
Classification |
|
🌟⬛️Locally Differentially Private Document Generation Using Zero Shot Prompting |
(EMNLP, 2023) |
|
Text Classification |
|
⬜️DP-forward: Fine-tuning and inference on language models with differential privacy in forward pass |
(SIGSAC, 2023) |
|
Classification |
|
⬛️InferDPT: Privacy-preserving Inference for Black-box Large Language Models |
2023.12 |
|
Classification, Generation |
|
⬜️Privacy-Preserving Prompt Tuning for Large Language Model Services |
2023.05 |
|
Sentiment Classification, Document Q&A |
|
⬛️Differential Privacy for Text Analytics via Natural Text Sanitization |
(ACL-IJCNLP, 2021) |
|
Classification |
|