The Privacy-Hallucination Tradeoff in Differentially Private Language Models
cs.AI, cs.CL
Submitted: 2026-08-31
Updated: 2026-08-31
Code: https://github.com/krramesh/privacy-hallucination-tradeoff
Terminology
Sources
- An Empirical Analysis of Fairness Notions under Differential Privacy
- Textual Unlearning Gives a False Sense of Unlearning
- Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
- Core: Robust Factual Precision with Informative Sub-Claim Identification
- Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy
- Harnessing large-language models to generate private synthetic text
- Scaling Laws for Differentially Private Language Models
- Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
- Understanding Factual Recall in Transformers via Associative Memories
- Reconstruction of Differentially Private Text Sanitization via Large Language Models
- Correlated Noise Mechanisms for Differentially Private Learning
- Fine-tuning Language Models for Factuality
- Opacus: User-Friendly Differential Privacy Library in PyTorch
- Long-form factuality in large language models
- Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
- Differentially Private Worst-group Risk Minimization
- Differentially-private text generation degrades output language quality
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