Cover Image

Privacy-aware Gaussian Process Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tuo, Rui, Chen, Haoyuan, Bhattacharya, Raktim
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)

Regression-aware Continual Learning for Android Malware Detection
by: Ghiani, Daniele, et al.
Published: (2025)

On the Privacy of Selection Mechanisms with Gaussian Noise
by: Lebensold, Jonathan, et al.
Published: (2024)

SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations
by: Tahera, Mohoshin Ara, et al.
Published: (2026)

An Efficient Privacy-aware Split Learning Framework for Satellite Communications
by: Sun, Jianfei, et al.
Published: (2024)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)

Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
by: Chiang, John
Published: (2022)

Privacy Amplification for the Gaussian Mechanism via Bounded Support
by: Hu, Shengyuan, et al.
Published: (2024)

On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel
by: Shukla, Shubhi, et al.
Published: (2022)

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
by: Liu, Terrance, et al.
Published: (2025)

Privacy Preserving Reinforcement Learning for Population Processes
by: Yang-Zhao, Samuel, et al.
Published: (2024)

Personalized Differential Privacy for Ridge Regression
by: Acharya, Krishna, et al.
Published: (2024)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
by: Ramesh, Vishal, et al.
Published: (2023)

Pure Differential Privacy for Functional Summaries with a Laplace-like Process
by: Lin, Haotian, et al.
Published: (2023)

Privacy-Preserving UCB Decision Process Verification via zk-SNARKs
by: Jiang, Xikun, et al.
Published: (2024)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
by: Meng, Jiayang, et al.
Published: (2025)

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
by: Xu, Jiahao, et al.
Published: (2025)

FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by: Gu, Yuechun, et al.
Published: (2024)

Differential Privacy Mechanisms in Neural Tangent Kernel Regression
by: Gu, Jiuxiang, et al.
Published: (2024)

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
by: Luo, Jinglong, et al.
Published: (2024)

Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
by: Chen, Wei-Ning, et al.
Published: (2024)

The Discrete Gaussian for Differential Privacy
by: Canonne, Clément L., et al.
Published: (2020)

Privacy Drift: Evolving Privacy Concerns in Incremental Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)

Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
by: Liu, Huikang, et al.
Published: (2026)

Calibrating Practical Privacy Risks for Differentially Private Machine Learning
by: Gu, Yuechun, et al.
Published: (2024)

PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
by: Flemings, James, et al.
Published: (2026)

Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
by: Dong, Andy, et al.
Published: (2025)

Differentially Private Post-Processing for Fair Regression
by: Xian, Ruicheng, et al.
Published: (2024)

Hessian-aware Training for Enhancing DNNs Resilience to Parameter Corruptions
by: Prato, Tahmid Hasan, et al.
Published: (2025)

Provable Privacy with Non-Private Pre-Processing
by: Hu, Yaxi, et al.
Published: (2024)

Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)

Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)

Empirical Privacy Variance
by: Hu, Yuzheng, et al.
Published: (2025)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
by: Pierquin, Clément, et al.
Published: (2023)

Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption
by: Lai, Zhizheng, et al.
Published: (2024)

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)

Can Copyright be Reduced to Privacy?
by: Elkin-Koren, Niva, et al.
Published: (2023)

Privacy Amplification for Matrix Mechanisms
by: Choquette-Choo, Christopher A., et al.
Published: (2023)

Privacy for Free in the Overparameterized Regime
by: Bombari, Simone, et al.
Published: (2024)