DP-FROST: Differentially Private Fine-tuning of Pre-trained Models with Freezing Model Parameters (2025.coling-main)
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| Challenge: | Training models with differential privacy has received a lot of attention since it provides theoretical guarantee of privacy preservation. |
| Approach: | They propose methods that fine-tune large-scale pre-trained models with freezing unimportant parameters for downstream tasks while satisfying differential privacy. |
| Outcome: | The proposed methods fine-tune large pre-trained models with freezing unimportant parameters while satisfying differential privacy while preserving their utility. |
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| Challenge: | Existing research on the efficiency of differentially-private stochastic gradient descent (DP-SGD) in NLP is inconclusive or even counter-intuitive. |
| Approach: | They propose to use differentially-private stochastic gradient descent (DP-SGD) to preserve privacy in NLP by using modern neural models based on BERT and XtremeDistil architectures to conduct extensive experiments. |
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Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation (2024.findings-emnlp)
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| Challenge: | Language models can memorize detailed information and patterns, but raise privacy concerns . ANADP reduces the performance gap between regular and DP fine-tuning while maintaining the privacy constraints. |
| Approach: | They propose an algorithm that allocates additive noise based on the importance of model parameters to reduce the performance gap between regular fine-tuning and traditional DP fine- tuning. |
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Training Text-to-Text Transformers with Privacy Guarantees (2022.findings-acl)
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| Challenge: | Recent advances in NLP often stem from large transformer-based pre-trained models. |
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Just Fine-tune Twice: Selective Differential Privacy for Large Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to protect language models from privacy leakage suffer from limited user control and low utility . et al., 2018: a novel framework that achieves SDP for state-of-the-art large transformer-based models. |
| Approach: | They propose a framework that applies differential privacy to large language models . they use redacted in-domain data to fine-tune the model with original in- domain data . |
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Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain (2023.findings-emnlp)
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| Challenge: | Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. |
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Differentially Private Learning Needs Better Model Initialization and Self-Distillation (2025.naacl-long)
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| Challenge: | Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. |
| Approach: | They propose a method that initializes a model using data synthesis from a small pre-trained LM with rigorous filtering, applies DP finetuning on private data, and performs self-distillation to refine outputs. |
| Outcome: | The proposed method outperforms vanilla DPSGD with significant improvements in lexical diversity and grammar errors. |
Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models (2025.naacl-long)
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| Challenge: | Existing methods for fine-tuning pre-trained models impose substantial resource usage. |
| Approach: | They propose a parameter-efficient fine-tuning method that freezes adapters early to reduce resource usage while maintaining performance. |
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How reparametrization trick broke differentially-private text representation learning (2022.acl-short)
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| Challenge: | Differential privacy (DP) is a formal mathematical treatment of privacy protection . it guarantees how much privacy can be lost in the worst case . adapting DP mechanisms to NLP properly is largely non-trivial task . |
| Approach: | They propose to use differential privacy to learn text representations using DPText to quantify and guarantee how much privacy can be lost in the worst case. |
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Differentially Private Knowledge Distillation via Synthetic Text Generation (2024.findings-acl)
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| Challenge: | Large Language models (LLMs) are achieving state-of-the-art performance in many downstream tasks, but data privacy is a major challenge for practitioners. |
| Approach: | They propose a differentially private knowledge distillation algorithm that exploits the knowledge of a teacher LLM and a student's output distribution. |
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Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
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Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |