Saved in:
| Main Authors: | Vinaroz, Margarita, Park, Mi Jung |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2301.13389 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023)
by: Liu, Michael F., et al.
Published: (2023)
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
by: Yang, Yilin, et al.
Published: (2023)
by: Yang, Yilin, et al.
Published: (2023)
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
by: Liu, Bochao, et al.
Published: (2024)
by: Liu, Bochao, et al.
Published: (2024)
Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
by: Liu, Bochao, et al.
Published: (2026)
by: Liu, Bochao, et al.
Published: (2026)
DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026)
by: Khadem, Fatemeh, et al.
Published: (2026)
DP-MGTD: Privacy-Preserving Machine-Generated Text Detection via Adaptive Differentially Private Entity Sanitization
by: Wang, Lionel Z., et al.
Published: (2026)
by: Wang, Lionel Z., et al.
Published: (2026)
DP-NCB: Privacy Preserving Fair Bandits
by: Sarkar, Dhruv, et al.
Published: (2025)
by: Sarkar, Dhruv, et al.
Published: (2025)
SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)
DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
by: Bosch, Marc Molina Van den, et al.
Published: (2026)
by: Bosch, Marc Molina Van den, et al.
Published: (2026)
DP-GPL: Differentially Private Graph Prompt Learning
by: Xu, Jing, et al.
Published: (2025)
by: Xu, Jing, et al.
Published: (2025)
DP-HYPE: Distributed Differentially Private Hyperparameter Search
by: Liebenow, Johannes, et al.
Published: (2025)
by: Liebenow, Johannes, et al.
Published: (2025)
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)
by: Carey, Alycia N., et al.
Published: (2024)
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation
by: Aoyama, Tatsuya, et al.
Published: (2025)
by: Aoyama, Tatsuya, et al.
Published: (2025)
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by: Piran, Fardin Jalil, et al.
Published: (2024)
by: Piran, Fardin Jalil, et al.
Published: (2024)
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
by: Tang, Xinyu, et al.
Published: (2023)
by: Tang, Xinyu, et al.
Published: (2023)
DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
by: Kim, Jihwan, et al.
Published: (2026)
by: Kim, Jihwan, et al.
Published: (2026)
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)
by: Tran, Khang, et al.
Published: (2023)
DP-TLDM: Differentially Private Tabular Latent Diffusion Model
by: Zhu, Chaoyi, et al.
Published: (2024)
by: Zhu, Chaoyi, et al.
Published: (2024)
Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
by: Wang, Tianyu, et al.
Published: (2026)
by: Wang, Tianyu, et al.
Published: (2026)
Differentially Private Kernelized Contextual Bandits
by: Pavlovic, Nikola, et al.
Published: (2025)
by: Pavlovic, Nikola, et al.
Published: (2025)
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
by: You, Jiang, et al.
Published: (2024)
by: You, Jiang, et al.
Published: (2024)
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
by: Fares, Mohamad Haj, et al.
Published: (2024)
by: Fares, Mohamad Haj, et al.
Published: (2024)
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset
by: Ray, Abhisek, et al.
Published: (2025)
by: Ray, Abhisek, et al.
Published: (2025)
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
by: Wang, Hui-Po, et al.
Published: (2023)
by: Wang, Hui-Po, et al.
Published: (2023)
DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2026)
by: Kalinin, Nikita P., et al.
Published: (2026)
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
by: Michel, Thomas, et al.
Published: (2025)
by: Michel, Thomas, et al.
Published: (2025)
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
Training Set Reconstruction from Differentially Private Forests: How Effective is DP?
by: Gorgé, Alice, et al.
Published: (2025)
by: Gorgé, Alice, et al.
Published: (2025)
BitNet Distillation
by: Wu, Xun, et al.
Published: (2025)
by: Wu, Xun, et al.
Published: (2025)
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)
by: Xu, Jie, et al.
Published: (2026)
Data-Distill-Net: A Data Distillation Approach Tailored for Reply-based Continual Learning
by: Liao, Wenyang, et al.
Published: (2025)
by: Liao, Wenyang, et al.
Published: (2025)
FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features
by: Zeng, Linghui, et al.
Published: (2025)
by: Zeng, Linghui, et al.
Published: (2025)
DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing
by: Saha, Utsab, et al.
Published: (2024)
by: Saha, Utsab, et al.
Published: (2024)
DP-aware AdaLN-Zero: Taming Conditioning-Induced Heavy-Tailed Gradients in Differentially Private Diffusion
by: Huang, Tao, et al.
Published: (2026)
by: Huang, Tao, et al.
Published: (2026)
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)
by: Gomez, Juan Felipe, et al.
Published: (2025)
SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)
by: Partohaghighi, Mohammad, et al.
Published: (2026)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Order-Preserving GFlowNets
by: Chen, Yihang, et al.
Published: (2023)
by: Chen, Yihang, et al.
Published: (2023)
Similar Items
-
DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023) -
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation
by: Yang, Yilin, et al.
Published: (2023) -
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation
by: Liu, Bochao, et al.
Published: (2024) -
Privacy-Preserving Model Transcription with Differentially Private Synthetic Distillation
by: Liu, Bochao, et al.
Published: (2026) -
DP-OPD: Differentially Private On-Policy Distillation for Language Models
by: Khadem, Fatemeh, et al.
Published: (2026)