Image de couverture de livre

RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Feng, Ce, Venkitasubramaniam, Parv
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
par: Wei, Chengkun, et autres
Publié: (2025)

PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
par: Sha, Haichao, et autres
Publié: (2023)

Have it your way: Individualized Privacy Assignment for DP-SGD
par: Boenisch, Franziska, et autres
Publié: (2023)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
par: Thudi, Anvith, et autres
Publié: (2023)

DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
par: Wei, Jianxin, et autres
Publié: (2022)

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
par: Zhang, Xinwei, et autres
Publié: (2023)

Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
par: Dong, Andy, et autres
Publié: (2026)

GeoClip: Geometry-Aware Clipping for Differentially Private SGD
par: Gilani, Atefeh, et autres
Publié: (2025)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
par: Steinke, Thomas, et autres
Publié: (2024)

An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
par: Liang, Hao, et autres
Publié: (2025)

Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
par: Chien, Eli, et autres
Publié: (2024)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
par: Kalinin, Nikita P., et autres
Publié: (2025)

How Private are DP-SGD Implementations?
par: Chua, Lynn, et autres
Publié: (2024)

Differentially Private Deep Model-Based Reinforcement Learning
par: Rio, Alexandre, et autres
Publié: (2024)

Differentially Private In-Context Learning with Nearest Neighbor Search
par: Koskela, Antti, et autres
Publié: (2025)

Differentially Private Model Merging
par: Yin, Qichuan, et autres
Publié: (2026)

Differentially Private Block-wise Gradient Shuffle for Deep Learning
par: Zagardo, David
Publié: (2024)

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
par: Piran, Fardin Jalil, et autres
Publié: (2024)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
par: Gao, Fengyu, et autres
Publié: (2024)

DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
par: Wu, Huiwen, et autres
Publié: (2024)

FlashDP: Private Training Large Language Models with Efficient DP-SGD
par: Wang, Liangyu, et autres
Publié: (2025)

Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
par: Ly, Timothée, et autres
Publié: (2024)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
par: Carey, Alycia N., et autres
Publié: (2024)

DP-SGD with weight clipping
par: Barczewski, Antoine, et autres
Publié: (2023)

Differentially Private Worst-group Risk Minimization
par: Zhou, Xinyu, et autres
Publié: (2024)

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization
par: Ertan, Murat Bilgehan, et autres
Publié: (2026)

Provable Differentially Private Computation of the Cross-Attention Mechanism
par: Ke, Yekun, et autres
Publié: (2024)

Improved Algorithms for Differentially Private Language Model Alignment
par: Chen, Keyu, et autres
Publié: (2025)

Differentially Private Iterative Screening Rules for Linear Regression
par: Khanna, Amol, et autres
Publié: (2025)

Evaluating Differentially Private Generation of Domain-Specific Text
par: Sun, Yidan, et autres
Publié: (2025)

Differentially Private Synthetic Data Release for Topics API Outputs
par: Dick, Travis, et autres
Publié: (2025)

Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
par: Shumailov, Ilia, et autres
Publié: (2025)

Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
par: Li, Yuecheng, et autres
Publié: (2024)

Reconstruction of Differentially Private Text Sanitization via Large Language Models
par: Pang, Shuchao, et autres
Publié: (2024)

Differentially Private Publication of Electricity Time Series Data in Smart Grids
par: Shaham, Sina, et autres
Publié: (2024)

Differentially Private Synthetic Data Generation Using Context-Aware GANs
par: Kotal, Anantaa, et autres
Publié: (2025)

Differentially Private Preference Data Synthesis for Large Language Model Alignment
par: Gao, Fengyu, et autres
Publié: (2026)

Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
par: Gomez, Juan Felipe, et autres
Publié: (2025)

Differentially Private Reinforcement Learning with Self-Play
par: Qiao, Dan, et autres
Publié: (2024)

On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
par: Kumar, Mohit, et autres
Publié: (2023)