{"total":41,"rows":[{"id":"2401.05778","title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","created":"2024-01-11","cited_by_count":66,"area":null},{"id":"2305.01639","title":"Privacy-Preserving In-Context Learning for Large Language Models","created":"2023-05-02","cited_by_count":50,"area":null},{"id":"2401.04343","title":"Private Fine-tuning of Large Language Models with Zeroth-order Optimization","created":"2024-01-09","cited_by_count":41,"area":null},{"id":"2302.09483","title":"Why Is Public Pretraining Necessary for Private Model Training?","created":"2023-02-19","cited_by_count":37,"area":null},{"id":"2307.08925","title":"Integration of Large Language Models and Federated Learning","created":"2023-07-18","cited_by_count":31,"area":null},{"id":"2305.12132","title":"Can Public Large Language Models Help Private Cross-device Federated Learning?","created":"2023-05-20","cited_by_count":30,"area":null},{"id":"2305.13865","title":"Selective Pre-training for Private Fine-tuning","created":"2023-05-23","cited_by_count":23,"area":null},{"id":"2402.02242","title":"Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark","created":"2024-02-03","cited_by_count":23,"area":null},{"id":"2301.09112","title":"Differentially Private Natural Language Models: Recent Advances and Future Directions","created":"2023-01-22","cited_by_count":20,"area":null},{"id":"2303.01256","title":"Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance","created":"2023-03-02","cited_by_count":16,"area":null},{"id":"2306.01181","title":"TMI! Finetuned Models Leak Private Information from their Pretraining Data","created":"2023-06-01","cited_by_count":14,"area":null},{"id":"2307.08122","title":"Tangent Transformers for Composition, Privacy and Removal","created":"2023-07-16","cited_by_count":13,"area":null},{"id":"2212.04486","title":"A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization","created":"2022-12-08","cited_by_count":11,"area":null},{"id":"2403.05681","title":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2402.18752","title":"Pre-training Differentially Private Models with Limited Public Data","created":"2024-02-28","cited_by_count":8,"area":null},{"id":"2405.08920","title":"Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning","created":"2024-05-14","cited_by_count":8,"area":null},{"id":"2506.07555","title":"Synthesize Privacy-Preserving High-Resolution Images via Private Textual Intermediaries","created":"2025-06-09","cited_by_count":8,"area":null},{"id":"2211.13403","title":"Differentially Private Image Classification from Features","created":"2022-11-24","cited_by_count":7,"area":null},{"id":"2310.19215","title":"On the accuracy and efficiency of group-wise clipping in differentially private optimization","created":"2023-10-30","cited_by_count":7,"area":null},{"id":"2311.11822","title":"Zero redundancy distributed learning with differential privacy","created":"2023-11-20","cited_by_count":7,"area":null},{"id":"2503.00703","title":"Towards hyperparameter-free optimization with differential privacy","created":"2025-03-02","cited_by_count":7,"area":null},{"id":"2211.08942","title":"Differentially Private Optimizers Can Learn Adversarially Robust Models","created":"2022-11-16","cited_by_count":6,"area":null},{"id":"2403.03856","title":"Public-data Assisted Private Stochastic Optimization: Power and Limitations","created":"2024-03-06","cited_by_count":6,"area":null},{"id":"2403.04124","title":"Privacy-preserving Fine-tuning of Large Language Models through Flatness","created":"2024-03-07","cited_by_count":6,"area":null},{"id":"2505.23724","title":"SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA","created":"2025-05-29","cited_by_count":5,"area":null},{"id":"2512.06711","title":"Parameter-Efficient Fine-Tuning with Differential Privacy for Robust Instruction Adaptation in Large Language Models","created":"2025-12-07","cited_by_count":5,"area":null},{"id":"2504.21036","title":"Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?","created":"2025-04-28","cited_by_count":4,"area":null},{"id":"2507.21117","title":"A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges","created":"2025-07-17","cited_by_count":3,"area":null},{"id":"2409.13953","title":"Training Large ASR Encoders with Differential Privacy","created":"2024-09-21","cited_by_count":2,"area":null},{"id":"2411.15831","title":"Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models","created":"2024-11-24","cited_by_count":2,"area":null},{"id":"2502.00451","title":"Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2510.07452","title":"PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing","created":"2025-10-08","cited_by_count":2,"area":null},{"id":"2511.20509","title":"DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning","created":"2025-11-25","cited_by_count":2,"area":null},{"id":"2605.03425","title":"FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2405.18776","title":"LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2508.15141","title":"Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2510.01137","title":"Re-examining Low Rank adaptation for private LLM fine-tuning","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2602.23516","title":"Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2608.17415","title":"Spectral Gradient Orthogonalization Improves Differentially Private Training at Scale","created":"2026-08-18","cited_by_count":0,"area":null},{"id":"2608.26655","title":"When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2610.17904","title":"Stable-Plastic Subspace Construction for Differentially Private Continual LoRA","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}