Tighter Privacy Auditing of Differentially Private Stochastic Gradient Descent in the Hidden State Threat Model
Fuente:
Zenodo
Saved in:
| Main Author: | Bhuekar, Apeksha |
|---|---|
| Format: | Recurso digital |
| Published: |
Zenodo
2026
|
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
by: Birrell, Jeremiah, et al.
Published: (2024)
by: Birrell, Jeremiah, et al.
Published: (2024)
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)
by: Yu, Da, et al.
Published: (2022)
Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)
by: Xia, Xintao, et al.
Published: (2025)
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
by: Brown, Gavin, et al.
Published: (2024)
by: Brown, Gavin, et al.
Published: (2024)
Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
by: Chien, Eli, et al.
Published: (2025)
by: Chien, Eli, et al.
Published: (2025)
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
by: Yoon, Sangyeon, et al.
Published: (2024)
by: Yoon, Sangyeon, et al.
Published: (2024)
Hidden State Differential Private Mini-Batch Block Coordinate Descent for Multi-convexity Optimization
by: Chen, Ding, et al.
Published: (2024)
by: Chen, Ding, et al.
Published: (2024)
Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
by: Dufraiche, Jean, et al.
Published: (2026)
by: Dufraiche, Jean, et al.
Published: (2026)
Tighter Bounds for Local Differentially Private Core Decomposition and Densest Subgraph
by: Henzinger, Monika, et al.
Published: (2024)
by: Henzinger, Monika, et al.
Published: (2024)
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent
by: Schertzer, Adrien, et al.
Published: (2024)
by: Schertzer, Adrien, et al.
Published: (2024)
Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
by: Liang, Haodong, et al.
Published: (2025)
by: Liang, Haodong, et al.
Published: (2025)
Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity
by: Li, Qiang, et al.
Published: (2024)
by: Li, Qiang, et al.
Published: (2024)
Optimizing Canaries for Privacy Auditing with Metagradient Descent
by: Boglioni, Matteo, et al.
Published: (2025)
by: Boglioni, Matteo, et al.
Published: (2025)
Auditing Privacy in Power Consumption Forecasting: Evaluating Gradient Leakage and Differential Privacy Defenses
by: Rixuan Qiu, et al.
Published: (2025)
by: Rixuan Qiu, et al.
Published: (2025)
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
by: Koloskova, Anastasia, et al.
Published: (2023)
by: Koloskova, Anastasia, et al.
Published: (2023)
Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)
by: Aujol, Jean-François, et al.
Published: (2025)
Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)
by: Birbil, S. Ilker, et al.
Published: (2021)
Stochastic Gradient Descent Revisited
by: Louzi, Azar
Published: (2024)
by: Louzi, Azar
Published: (2024)
Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
by: Wang, Puyu, et al.
Published: (2026)
by: Wang, Puyu, et al.
Published: (2026)
Auditing Approximate Machine Unlearning for Differentially Private Models
by: Gu, Yuechun, et al.
Published: (2025)
by: Gu, Yuechun, et al.
Published: (2025)
Online Quantum State Tomography via Stochastic Gradient Descent
by: Cai, Jian-Feng, et al.
Published: (2025)
by: Cai, Jian-Feng, et al.
Published: (2025)
Sequentially Auditing Differential Privacy
by: González, Tomás, et al.
Published: (2025)
by: González, Tomás, et al.
Published: (2025)
Parameter Estimation in Stochastic Differential Equations via Wiener Chaos Expansion and Stochastic Gradient Descent
by: Delgado-Vences, Francisco, et al.
Published: (2026)
by: Delgado-Vences, Francisco, et al.
Published: (2026)
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)
by: Yu, Siyuan, et al.
Published: (2024)
Revisiting Stochastic Approximation and Stochastic Gradient Descent
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)
by: Karandikar, Rajeeva Laxman, et al.
Published: (2025)
Stochastic versus Deterministic in Stochastic Gradient Descent
by: Li, Runze, et al.
Published: (2025)
by: Li, Runze, et al.
Published: (2025)
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
by: Lin, Shurong, et al.
Published: (2025)
by: Lin, Shurong, et al.
Published: (2025)
Towards Learning Stochastic Population Models by Gradient Descent
by: Kreikemeyer, Justin N., et al.
Published: (2024)
by: Kreikemeyer, Justin N., et al.
Published: (2024)
DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication
by: Zhu, Zehan, et al.
Published: (2025)
by: Zhu, Zehan, et al.
Published: (2025)
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)
by: Mukherjee, Amartya, et al.
Published: (2025)
Tighter Privacy Analysis for Truncated Poisson Sampling
by: Ganesh, Arun
Published: (2025)
by: Ganesh, Arun
Published: (2025)
Differentially Private Random Block Coordinate Descent
by: Maranjyan, Artavazd, et al.
Published: (2024)
by: Maranjyan, Artavazd, et al.
Published: (2024)
Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)
by: Che, Ethan, et al.
Published: (2024)
Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)
by: Jiang, Nanfei, et al.
Published: (2025)
On the different regimes of Stochastic Gradient Descent
by: Sclocchi, Antonio, et al.
Published: (2023)
by: Sclocchi, Antonio, et al.
Published: (2023)
On the Generalization of Stochastic Gradient Descent with Momentum
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)
Differentially Private Auditing Under Strategic Response
by: Burnat, Florian A. D.
Published: (2026)
by: Burnat, Florian A. D.
Published: (2026)
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
by: Gess, Benjamin, et al.
Published: (2024)
by: Gess, Benjamin, et al.
Published: (2024)
Sequential Auditing for f-Differential Privacy
by: Kutta, Tim, et al.
Published: (2026)
by: Kutta, Tim, et al.
Published: (2026)
Similar Items
-
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024) -
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
by: Birrell, Jeremiah, et al.
Published: (2024) -
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022) -
Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025) -
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
by: Brown, Gavin, et al.
Published: (2024)