Cover Image

Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Chiang, John
Format:	Preprint
Published:	2022
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression
by: Chiang, John
Published: (2023)

Privacy-Preserving Logistic Regression Training on Large Datasets
by: Chiang, John
Published: (2024)

Privacy-Preserving 3-Layer Neural Network Training
by: Chiang, John
Published: (2023)

LFFR: Logistic Function For (single-output) Regression
by: Chiang, John
Published: (2024)

SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
by: Wang, Hongxiao, et al.
Published: (2022)

LFFR: Logistic Function For (multi-output) Regression
by: Chiang, John
Published: (2024)

Privacy-Preserving CNN Training with Transfer Learning: Two Hidden Layers
by: Chiang, John
Published: (2025)

Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
by: Greenewald, Kristjan, et al.
Published: (2024)

Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
by: Hoseinpour, Mohammad, et al.
Published: (2023)

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

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods
by: Ullah, Mohammad Arafat, et al.
Published: (2024)

Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
by: Chen, Congcong, et al.
Published: (2025)

A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
by: Wang, Huan-Chih, et al.
Published: (2024)

Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
by: Cartmell, John, et al.
Published: (2026)

Privacy-aware Gaussian Process Regression
by: Tuo, Rui, et al.
Published: (2023)

Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)

Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)

Data Privacy Preservation on the Internet of Things
by: Sen, Jaydip, et al.
Published: (2023)

Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning
by: Yang, Xue, et al.
Published: (2024)

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

Privacy-Preserving Generative Models: A Comprehensive Survey
by: Padariya, Debalina, et al.
Published: (2025)

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

Privacy-Preserving Inference for Quantized BERT Models
by: Lu, Tianpei, et al.
Published: (2025)

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

Privacy-Preserving Vertical K-Means Clustering
by: Mazzone, Federico, et al.
Published: (2025)

Privacy-Preserving Fair Synthetic Tabular Data
by: Sarmin, Fatima J., et al.
Published: (2025)

Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)

Privacy-Preserving Hierarchical Model-Distributed Inference
by: Dehkordi, Fatemeh Jafarian, 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)

Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)

Privacy-Preserving Optimal Parameter Selection for Collaborative Clustering
by: Ghasemian, Maryam, et al.
Published: (2024)

Privacy-Preserving Verifiable Neural Network Inference Service
by: Riasi, Arman, et al.
Published: (2024)

Automated Privacy-Preserving Techniques via Meta-Learning
by: Carvalho, Tânia, et al.
Published: (2024)

Preserving Expert-Level Privacy in Offline Reinforcement Learning
by: Sharma, Navodita, et al.
Published: (2024)

opp/ai: Optimistic Privacy-Preserving AI on Blockchain
by: So, Cathie, et al.
Published: (2024)

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)

Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations
by: Westrich, Benjamin
Published: (2025)

LDPKiT: Superimposing Remote Queries for Privacy-Preserving Local Model Training
by: Li, Kexin, et al.
Published: (2024)

Social-Aware Clustered Federated Learning with Customized Privacy Preservation
by: Wang, Yuntao, et al.
Published: (2022)

Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning
by: Hayati, Haleh, et al.
Published: (2024)