SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Partohaghighi, Mohammad, Marcia, Roummel |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deep Learning under Fractional-Order Differential Privacy
by: Partohaghighi, Mohammad, et al.
Published: (2026)
by: Partohaghighi, Mohammad, 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)
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)
by: Tran, Khang, et al.
Published: (2023)
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
by: Liu, Yanming, et al.
Published: (2024)
by: Liu, Yanming, et al.
Published: (2024)
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)
When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)
by: Partohaghighi, Mohammad, et al.
Published: (2026)
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
by: Saha, Aadirupa, et al.
Published: (2024)
by: Saha, Aadirupa, et al.
Published: (2024)
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
by: Ertan, Murat Bilgehan, et al.
Published: (2026)
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
by: Zhou, Yan, et al.
Published: (2025)
by: Zhou, Yan, et al.
Published: (2025)
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
by: Pathegama, Madhura, et al.
Published: (2026)
by: Pathegama, Madhura, et al.
Published: (2026)
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
by: Buchholz, Erik, et al.
Published: (2025)
by: Buchholz, Erik, et al.
Published: (2025)
DP-LDMs: Differentially Private Latent Diffusion Models
by: Liu, Michael F., et al.
Published: (2023)
by: Liu, Michael F., et al.
Published: (2023)
DP-KAN: Differentially Private Kolmogorov-Arnold Networks
by: Kalinin, Nikita P., et al.
Published: (2024)
by: Kalinin, Nikita P., et al.
Published: (2024)
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024)
by: Cebere, Tudor, et al.
Published: (2024)
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
by: Wei, Yuecen, et al.
Published: (2023)
by: Wei, Yuecen, 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)
Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection
by: Sana, Joydeb Kumar
Published: (2026)
by: Sana, Joydeb Kumar
Published: (2026)
Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
by: Ngo, Hoang M., et al.
Published: (2026)
by: Ngo, Hoang M., et al.
Published: (2026)
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
by: Wu, Haoqi, et al.
Published: (2025)
by: Wu, Haoqi, et al.
Published: (2025)
It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)
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)
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
by: Ponomareva, Natalia, et al.
Published: (2025)
by: Ponomareva, Natalia, et al.
Published: (2025)
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)
by: You, Zhichao, et al.
Published: (2025)
PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
by: Yang, Qin, et al.
Published: (2025)
by: Yang, Qin, et al.
Published: (2025)
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models
by: Sha, Haichao, et al.
Published: (2026)
by: Sha, Haichao, 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)
Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)
by: Yang, Chengyi, et al.
Published: (2024)
Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)
by: Sun, Yan, et al.
Published: (2024)
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
by: Islam, MD Shafikul, et al.
Published: (2026)
by: Islam, MD Shafikul, et al.
Published: (2026)
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, 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)
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
by: Schuchardt, Jan, et al.
Published: (2025)
by: Schuchardt, Jan, et al.
Published: (2025)
SafeLM: Unified Privacy-Aware Optimization for Trustworthy Federated Large Language Models
by: Mohammad, Noor Islam S., et al.
Published: (2026)
by: Mohammad, Noor Islam S., et al.
Published: (2026)
Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
by: Bai, Shaojie, et al.
Published: (2024)
by: Bai, Shaojie, et al.
Published: (2024)
FlashDP: Private Training Large Language Models with Efficient DP-SGD
by: Wang, Liangyu, et al.
Published: (2025)
by: Wang, Liangyu, et al.
Published: (2025)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy
by: Oliveira, Júlio, et al.
Published: (2026)
by: Oliveira, Júlio, et al.
Published: (2026)
Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)
by: Huang, Yinan, et al.
Published: (2025)
Complex-valued Federated Learning with Differential Privacy and MRI Applications
by: Riess, Anneliese, et al.
Published: (2021)
by: Riess, Anneliese, et al.
Published: (2021)
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
Similar Items
-
Deep Learning under Fractional-Order Differential Privacy
by: Partohaghighi, Mohammad, et al.
Published: (2026) -
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025) -
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023) -
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
by: Liu, Yanming, et al.
Published: (2024) -
SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)