Private Linear Regression with Differential Privacy and PAC Privacy
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Hillary, Du, Yuntao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023)
by: Redberg, Rachel, et al.
Published: (2023)
Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?
by: Du, Hao, et al.
Published: (2025)
by: Du, Hao, et al.
Published: (2025)
Beyond Data Privacy: New Privacy Risks for Large Language Models
by: Du, Yuntao, et al.
Published: (2025)
by: Du, Yuntao, et al.
Published: (2025)
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)
by: Gu, Yuechun, et al.
Published: (2024)
Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)
by: Huang, Yinan, et al.
Published: (2025)
Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)
by: Yang, Chengyi, et al.
Published: (2024)
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)
by: Tian, Xizhi, et al.
Published: (2025)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, 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)
Personalized Differential Privacy for Ridge Regression
by: Acharya, Krishna, et al.
Published: (2024)
by: Acharya, Krishna, et al.
Published: (2024)
Privacy Profiles for Private Selection
by: Koskela, Antti, et al.
Published: (2024)
by: Koskela, Antti, et al.
Published: (2024)
Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)
by: Pierquin, Clément, et al.
Published: (2025)
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)
by: Zhang, Xinwei, 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)
Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)
by: Tobaben, Marlon, 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)
An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
by: Liang, Hao, et al.
Published: (2025)
by: Liang, Hao, 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)
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)
Differentially Private Iterative Screening Rules for Linear Regression
by: Khanna, Amol, et al.
Published: (2025)
by: Khanna, Amol, et al.
Published: (2025)
Differential Privacy Mechanisms in Neural Tangent Kernel Regression
by: Gu, Jiuxiang, et al.
Published: (2024)
by: Gu, Jiuxiang, et al.
Published: (2024)
Privacy-aware Gaussian Process Regression
by: Tuo, Rui, et al.
Published: (2023)
by: Tuo, Rui, et al.
Published: (2023)
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
by: Vithana, Sajani, et al.
Published: (2024)
by: Vithana, Sajani, et al.
Published: (2024)
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)
PAC-Private Responses with Adversarial Composition
by: Zhu, Xiaochen, et al.
Published: (2026)
by: Zhu, Xiaochen, et al.
Published: (2026)
Differentially Private Inference for Longitudinal Linear Regression
by: Sopa, Getoar, et al.
Published: (2026)
by: Sopa, Getoar, et al.
Published: (2026)
Private Estimation when Data and Privacy Demands are Correlated
by: Chaudhuri, Syomantak, et al.
Published: (2024)
by: Chaudhuri, Syomantak, et al.
Published: (2024)
Laplace Transform Interpretation of Differential Privacy
by: Chourasia, Rishav, et al.
Published: (2024)
by: Chourasia, Rishav, et al.
Published: (2024)
Contrastive Explainable Clustering with Differential Privacy
by: Nguyen, Dung, et al.
Published: (2024)
by: Nguyen, Dung, et al.
Published: (2024)
Limits of Personalizing Differential Privacy Budgets
by: Cyffers, Edwige, et al.
Published: (2026)
by: Cyffers, Edwige, et al.
Published: (2026)
Setting $\varepsilon$ is not the Issue in Differential Privacy
by: Cyffers, Edwige
Published: (2025)
by: Cyffers, Edwige
Published: (2025)
Revisiting Hyperparameter Tuning with Differential Privacy
by: Ding, Youlong, et al.
Published: (2022)
by: Ding, Youlong, et al.
Published: (2022)
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)
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)
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)
by: Yu, Da, et al.
Published: (2022)
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
by: Wang, Zhanyu, et al.
Published: (2022)
by: Wang, Zhanyu, et al.
Published: (2022)
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
by: Wang, Yuntao, et al.
Published: (2022)
by: Wang, Yuntao, et al.
Published: (2022)
Auditing $f$-Differential Privacy in One Run
by: Mahloujifar, Saeed, et al.
Published: (2024)
by: Mahloujifar, Saeed, et al.
Published: (2024)
Sequentially Auditing Differential Privacy
by: González, Tomás, et al.
Published: (2025)
by: González, Tomás, et al.
Published: (2025)
Differential Privacy in the Extensive-Form Bandit Problem
by: Pasteris, Stephen, et al.
Published: (2026)
by: Pasteris, Stephen, et al.
Published: (2026)
Similar Items
-
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023) -
Can Differentially Private Fine-tuning LLMs Protect Against Privacy Attacks?
by: Du, Hao, et al.
Published: (2025) -
Beyond Data Privacy: New Privacy Risks for Large Language Models
by: Du, Yuntao, et al.
Published: (2025) -
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024) -
Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)