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Differentially Private Fine-tuning of Language Models

https://doi.org/10.29012/jpc.880
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/11761679_29openalex-snapshot
Our Data, Ourselves: Privacy Via Distributed Noise Generation
inferredcites →https://doi.org/10.1007/978-3-642-40328-6_26openalex-snapshot
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
inferredcites →https://doi.org/10.1007/s10994-013-5396-xopenalex-snapshot
Differential privacy based on importance weighting
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00502openalex-snapshot
Scalable Differential Privacy with Sparse Network Finetuning
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00819openalex-snapshot
Mixed Differential Privacy in Computer Vision
inferredcites →https://doi.org/10.1109/globalsip.2013.6736861openalex-snapshot
Stochastic gradient descent with differentially private updates
inferredcites →https://doi.org/10.1145/2591796.2591877openalex-snapshot
Fingerprinting codes and the price of approximate differential privacy
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3357713.3384290openalex-snapshot
Does learning require memorization? a short tale about a long tail
inferredcites →https://doi.org/10.1145/3406325.3451131openalex-snapshot
When is memorization of irrelevant training data necessary for high-accuracy learning?
inferredcites →https://doi.org/10.1145/3576915.3616592openalex-snapshot
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
inferredcites →https://doi.org/10.1162/99608f92.d3197524openalex-snapshot
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
inferredcites →https://doi.org/10.14288/1.0377599openalex-snapshot
Model-Agnostic Private Learning
inferredcites →https://doi.org/10.18653/v1/2020.privatenlp-1openalex-snapshot
Proceedings of the Second Workshop on Privacy in NLP
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.353openalex-snapshot
Prefix-Tuning: Optimizing Continuous Prompts for Generation
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.568openalex-snapshot
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
inferredcites →https://doi.org/10.18653/v1/2021.eacl-mainopenalex-snapshot
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.243openalex-snapshot
The Power of Scale for Parameter-Efficient Prompt Tuning
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.626openalex-snapshot
AdapterDrop: On the Efficiency of Adapters in Transformers
inferredcites →https://doi.org/10.18653/v1/2021.findings-emnlp.102openalex-snapshot
Learning and Evaluating a Differentially Private Pre-trained Language Model
inferredcites →https://doi.org/10.18653/v1/2021.naacl-mainopenalex-snapshot
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
inferredcites →https://doi.org/10.18653/v1/2022.acl-shortopenalex-snapshot
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
inferredcites →https://doi.org/10.18653/v1/2022.emnlp-main.496openalex-snapshot
One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks
inferredcites →https://doi.org/10.18653/v1/2022.findings-emnlp.484openalex-snapshot
Large-Scale Differentially Private BERT
inferredcites →https://doi.org/10.18653/v1/2023.acl-longopenalex-snapshot
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
inferredcites →https://doi.org/10.18653/v1/n18-1101openalex-snapshot
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.18653/v1/w17-5525openalex-snapshot
The E2E Dataset: New Challenges For End-to-End Generation
inferredcites →https://doi.org/10.18653/v1/w18-5446openalex-snapshot
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
inferredcites →https://doi.org/10.18653/v1/w18-6301openalex-snapshot
Scaling Neural Machine Translation
inferredcites →https://doi.org/10.29012/jpc.870openalex-snapshot
Numerical Composition of Differential Privacy
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-snapshot
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.4230/lipics.giscience.2023.43openalex-snapshot
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1606.08415openalex-snapshot
Gaussian Error Linear Units (GELUs)
inferredcites →https://doi.org/10.48550/arxiv.1710.03740openalex-snapshot
Mixed Precision Training
inferredcites →https://doi.org/10.48550/arxiv.1710.06963openalex-snapshot
Learning Differentially Private Recurrent Language Models
inferredcites →https://doi.org/10.48550/arxiv.1711.05101openalex-snapshot
Decoupled Weight Decay Regularization
inferredcites →https://doi.org/10.48550/arxiv.1802.08232openalex-snapshot
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1802.08908openalex-snapshot
Scalable Private Learning with PATE
inferredcites →https://doi.org/10.48550/arxiv.1906.03049openalex-snapshot
Computing Tight Differential Privacy Guarantees Using FFT
inferredcites →https://doi.org/10.48550/arxiv.1907.13553openalex-snapshot
Privately Answering Classification Queries in the Agnostic PAC Model
inferredcites →https://doi.org/10.48550/arxiv.2007.03813openalex-snapshot
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
inferredcites →https://doi.org/10.48550/arxiv.2008.00331openalex-snapshot
Learning from Mixtures of Private and Public Populations
inferredcites →https://doi.org/10.48550/arxiv.2011.11660openalex-snapshot
Differentially Private Learning Needs Better Features (or Much More Data)
inferredcites →https://doi.org/10.48550/arxiv.2102.08598openalex-snapshot
Leveraging Public Data for Practical Private Query Release
inferredcites →https://doi.org/10.48550/arxiv.2102.12677openalex-snapshot
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
inferredcites →https://doi.org/10.48550/arxiv.2106.09352openalex-snapshot
Large Scale Private Learning via Low-rank Reparametrization
inferredcites →https://doi.org/10.48550/arxiv.2106.09685openalex-snapshot
LoRA Fine-Tuning of a 3B Code LLM for Algorithmic Efficiency
inferredcites →https://doi.org/10.48550/arxiv.2110.05679openalex-snapshot
Large Language Models Can Be Strong Differentially Private Learners
inferredcites →https://doi.org/10.48550/arxiv.2201.00971openalex-snapshot
Submix: Practical Private Prediction for Large-Scale Language Models
inferredcites →https://doi.org/10.48550/arxiv.2204.13650openalex-snapshot
Unlocking High-Accuracy Differentially Private Image Classification through Scale
inferredcites →https://doi.org/10.48550/arxiv.2205.02973openalex-snapshot
Large Scale Transfer Learning for Differentially Private Image Classification
inferredcites →https://doi.org/10.48550/arxiv.2205.12900openalex-snapshot
Pre-trained Perceptual Features Improve Differentially Private Image Generation
inferredcites →https://doi.org/10.48550/arxiv.2210.00036openalex-snapshot
Differentially Private Bias-Term Fine-tuning of Foundation Models
inferredcites →https://doi.org/10.48550/arxiv.2210.00038openalex-snapshot
Differentially Private Optimization on Large Model at Small Cost
inferredcites →https://doi.org/10.48550/arxiv.2212.01539openalex-snapshot
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
inferredcites →https://doi.org/10.48550/arxiv.2212.06470openalex-snapshot
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
inferredcites →https://doi.org/10.48550/arxiv.2302.13861openalex-snapshot
Differentially Private Diffusion Models Generate Useful Synthetic Images
inferredcites →https://doi.org/10.48550/arxiv.2306.01684openalex-snapshot
Harnessing large-language models to generate private synthetic text
inferredcites →https://doi.org/10.48550/arxiv.2308.10888openalex-snapshot
Unlocking Accuracy and Fairness in Differentially Private Image Classification
inferredcites →https://doi.org/10.48550/arxiv.2309.00008openalex-snapshot
Large-Scale Public Data Improves Differentially Private Image Generation Quality
inferredcites →https://doi.org/10.48550/arxiv.2310.16960openalex-snapshot
Privately Aligning Language Models with Reinforcement Learning
inferredcites →https://doi.org/10.52202/068431-2074openalex-snapshot
When Does Differentially Private Learning Not Suffer in High Dimensions?
inferredcites →https://doi.org/10.52202/068431-2137openalex-snapshot
Differentially Private Model Compression
inferredcites →https://doi.org/10.52202/085713-3706openalex-snapshot
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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AI Sovereignty: Redundant Use of Large Language Models for Public Sector Resilience
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Privacy-aware adversarial defense with explainable AI for adversarial robustness in AI model
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DPRO-GNN: Bridging differential privacy and advanced optimization for privacy-preserving graph learning
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Accuracy-aware differential privacy in federated learning of large transformer models
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Privacy for free in the overparameterized regime
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A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?
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Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
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Improving Streaming ASR via Differentially Private Fusion of Data from Multiple Sources
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Privacy-Preserving Federated Learning for Science: Challenges and Research Directions
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A Distributed Multi-Agent Framework for Real-Time Privacy Monitoring and Compliance in Enterprise AI Systems
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Sentiment Analysis with LLMs: Evaluating QLoRA Fine-Tuning, Instruction Strategies, and Prompt Sensitivity
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Decision-Making Large Language Model for Wireless Communication: A Comprehensive Survey on Key Techniques
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LAP 2 : Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
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Learning to Generate Image Embeddings with User-Level Differential Privacy
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A Survey of Privacy Preservation Techniques for Large Language Models
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Modelling direct messaging networks with multiple recipients for cyber deception
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Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10888369openalex-snapshot
FedTLU: Federated Learning with Targeted Layer Updates
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Exploring the Benefits of Visual Prompting in Differential Privacy
inferred← citeshttps://doi.org/10.1109/icdmw58026.2022.00078openalex-snapshot
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11227270openalex-snapshot
Are Task-Specific Datasets the Be-All and End-All for LLM Performance?
inferred← citeshttps://doi.org/10.1109/jiot.2024.3496920openalex-snapshot
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
inferred← citeshttps://doi.org/10.1109/jiot.2026.3655343openalex-snapshot
Generative AI-Driven Digital Twin in the Manufacturing Internet of Things: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/qrs-c65679.2025.00018openalex-snapshot
FL-DPLoRA: An Integrated and Efficient Privacy-Preserving Training Framework for Large Language Models in Privacy-Critical Applications
inferred← citeshttps://doi.org/10.1109/satml54575.2023.00022openalex-snapshot
Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming
inferred← citeshttps://doi.org/10.1109/satml59370.2024.00010openalex-snapshot
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
inferred← citeshttps://doi.org/10.1109/satml64287.2025.00054openalex-snapshot
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
inferred← citeshttps://doi.org/10.1109/satml68715.2026.00033openalex-snapshot
Position: Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
inferred← citeshttps://doi.org/10.1109/slt61566.2024.10832249openalex-snapshot
Training Large ASR Encoders With Differential Privacy
inferred← citeshttps://doi.org/10.1109/sp46215.2023.10179300openalex-snapshot
Analyzing Leakage of Personally Identifiable Information in Language Models
inferred← citeshttps://doi.org/10.1109/sp46215.2023.10179422openalex-snapshot
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
inferred← citeshttps://doi.org/10.1109/sp54263.2024.00270openalex-snapshot
Preserving Node-level Privacy in Graph Neural Networks
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Rigging the Foundation: Manipulating Pre-training for Advanced Membership Inference Attacks
inferred← citeshttps://doi.org/10.1109/sp61157.2025.00241openalex-snapshot
Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms
inferred← citeshttps://doi.org/10.1109/spw67851.2025.00035openalex-snapshot
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
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Fostering Human Rights in Responsible AI: A Systematic Review for Best Practices in Industry
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VFFG: Verifiable Privacy-Enhanced Federated Fine-Tuning for GPT Service
inferred← citeshttps://doi.org/10.1109/tifs.2025.3557755openalex-snapshot
DC-SGD: Differentially Private SGD With Dynamic Clipping Through Gradient Norm Distribution Estimation
inferred← citeshttps://doi.org/10.1109/tifs.2025.3581103openalex-snapshot
Hardening LLM Fine-Tuning: From Differentially Private Data Selection to Trustworthy Model Quantization
inferred← citeshttps://doi.org/10.1109/tifs.2026.3679194openalex-snapshot
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Fine-Tuning
inferred← citeshttps://doi.org/10.1109/tifs.2026.3688926openalex-snapshot
K -TCDP: A Temporal Correlated DP Mechanism for LoRA Supervised Fine-Tuning
inferred← citeshttps://doi.org/10.1109/tnse.2025.3610125openalex-snapshot
SLDP-LoRA: A Privacy-Preserving Split Learning Framework With Low-Rank Adaptation
inferred← citeshttps://doi.org/10.1109/ton.2025.3548277openalex-snapshot
AugFL: Augmenting Federated Learning With Pretrained Models
inferred← citeshttps://doi.org/10.1109/ton.2026.3658317openalex-snapshot
Privacy-Utility Trade-Off in Federated LLM Fine-Tuning: A Dynamic Game Approach
inferred← citeshttps://doi.org/10.1109/tps-isa62245.2024.00038openalex-snapshot
Patient-Centered and Practical Privacy to Support AI for Healthcare
inferred← citeshttps://doi.org/10.1109/twc.2025.3585291openalex-snapshot
PrivTuner With Homomorphic Encryption and LoRA: A P3EFT Scheme for Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
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My3DGen: A Scalable Personalized 3D Generative Model
inferred← citeshttps://doi.org/10.1145/3576915.3616592openalex-snapshot
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
inferred← citeshttps://doi.org/10.1145/3733799.3762984openalex-snapshot
DP-Morph: Improving the Privacy-Utility-Performance Trade-off for Differentially Private OCT Segmentation
inferred← citeshttps://doi.org/10.1145/3777490.3777507openalex-snapshot
Right-to-be-Forgotten by Design in Adapter-Tuned Transformers
inferred← citeshttps://doi.org/10.1162/99608f92.d3197524openalex-snapshot
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
inferred← citeshttps://doi.org/10.1609/aaai.v37i9.26242openalex-snapshot
Differentially Private Learning with Per-Sample Adaptive Clipping
inferred← citeshttps://doi.org/10.1609/aaai.v38i19.30136openalex-snapshot
SentinelLMs: Encrypted Input Adaptation and Fine-Tuning of Language Models for Private and Secure Inference
inferred← citeshttps://doi.org/10.1613/jair.1.14649openalex-snapshot
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
inferred← citeshttps://doi.org/10.18653/v1/2022.emnlp-main.323openalex-snapshot
Differentially Private Language Models for Secure Data Sharing
inferred← citeshttps://doi.org/10.18653/v1/2022.emnlp-main.425openalex-snapshot
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
inferred← citeshttps://doi.org/10.18653/v1/2022.findings-emnlp.148openalex-snapshot
Are Large Pre-Trained Language Models Leaking Your Personal Information?
inferred← citeshttps://doi.org/10.18653/v1/2022.findings-naacl.65openalex-snapshot
The Limits of Word Level Differential Privacy
inferred← citeshttps://doi.org/10.18653/v1/2022.naacl-srw.22openalex-snapshot
Differentially Private Instance Encoding against Privacy Attacks
inferred← citeshttps://doi.org/10.18653/v1/2022.privatenlp-1openalex-snapshot
Proceedings of the Fourth Workshop on Privacy in Natural Language Processing
inferred← citeshttps://doi.org/10.18653/v1/2022.privatenlp-1.1openalex-snapshot
Differential Privacy in Natural Language Processing The Story So Far
inferred← citeshttps://doi.org/10.18653/v1/2023.acl-long.271openalex-snapshot
Privacy-Preserving Domain Adaptation of Semantic Parsers
inferred← citeshttps://doi.org/10.18653/v1/2023.acl-long.74openalex-snapshot
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
inferred← citeshttps://doi.org/10.18653/v1/2023.emnlp-main.265openalex-snapshot
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models
inferred← citeshttps://doi.org/10.18653/v1/2023.emnlp-main.738openalex-snapshot
Unlearn What You Want to Forget: Efficient Unlearning for LLMs
inferred← citeshttps://doi.org/10.18653/v1/2023.emnlp-main.921openalex-snapshot
Privacy Implications of Retrieval-Based Language Models
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-acl.434openalex-snapshot
Trade-Offs Between Fairness and Privacy in Language Modeling
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-acl.799openalex-snapshot
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-emnlp.402openalex-snapshot
Domain Private Transformers for Multi-Domain Dialog Systems
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-emnlp.566openalex-snapshot
Locally Differentially Private Document Generation Using Zero Shot Prompting
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-emnlp.872openalex-snapshot
LLMaAA: Making Large Language Models as Active Annotators
inferred← citeshttps://doi.org/10.18653/v1/2023.findings-ijcnlp.25openalex-snapshot
Privacy Adhering Machine Un-learning in NLP
inferred← citeshttps://doi.org/10.18653/v1/2023.inlg-main.3openalex-snapshot
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy
inferred← citeshttps://doi.org/10.18653/v1/2023.trustnlp-1.23openalex-snapshot
Training Data Extraction From Pre-trained Language Models: A Survey
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1682972/v1openalex-snapshot
Privacy-preserving, Efficient, and Effective Machine Learning
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Foundation Models and Fair Use
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Color-Dp: A Conjugate Low-Rank Optimized Dp Mechanism for Lora Fine-Tuning
inferred← citeshttps://doi.org/10.21437/interspeech.2023-551openalex-snapshot
Differentially Private Adapters for Parameter Efficient Acoustic Modeling
inferred← citeshttps://doi.org/10.22215/etd/2022-15201openalex-snapshot
FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition
inferred← citeshttps://doi.org/10.3390/electronics15050943openalex-snapshot
PP-EDUVec: Privacy-Preserving Intelligent Management Algorithms for Educational-Corpus Vector Databases Under Retrieval-Augmented Learning
inferred← citeshttps://doi.org/10.35542/osf.io/rw45bopenalex-snapshot
A Step Towards Adaptive Online Learning: Exploring the Role of GPT as Virtual Teaching Assistants in Online Education
inferred← citeshttps://doi.org/10.36227/techrxiv.24191886openalex-snapshot
Privacy-Preserving Fine-Tuning of Artificial Intelligence (AI) Foundation Models with Federated Learning, Differential Privacy, Offsite Tuning, and Parameter-Efficient Fine-Tuning (PEFT)
inferred← citeshttps://doi.org/10.36227/techrxiv.24191886.v1openalex-snapshot
Privacy-Preserving Fine-Tuning of Artificial Intelligence (AI) Foundation Models with Federated Learning, Differential Privacy, Offsite Tuning, and Parameter-Efficient Fine-Tuning (PEFT)
inferred← citeshttps://doi.org/10.48550/arxiv.2111.06578openalex-snapshot
Differential privacy and robust statistics in high dimensions
inferred← citeshttps://doi.org/10.48550/arxiv.2112.00193openalex-snapshot
Public Data-Assisted Mirror Descent for Private Model Training
inferred← citeshttps://doi.org/10.48550/arxiv.2308.08774openalex-snapshot
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models
inferred← citeshttps://doi.org/10.54097/we271q84openalex-snapshot
Efficient Differentially Private Fine-Tuning with QLoRA and Prefix Tuning for Large Language Models
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