Cover Image

Efficient Privacy-Preserving Machine Learning with Lightweight Trusted Hardware

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Pengzhi, Hoang, Thang, Li, Yueying, Shi, Elaine, Suh, G. Edward
Format:	Preprint
Published:	2022
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
by: Behnia, Rouzbeh, et al.
Published: (2023)

Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
by: Huang, Pengzhi, et al.
Published: (2026)

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

Machine Learning with Privacy for Protected Attributes
by: Mahloujifar, Saeed, et al.
Published: (2025)

Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy
by: Behnia, Rouzbeh, et al.
Published: (2025)

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

The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries
by: Lu, Tianpei, et al.
Published: (2024)

Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
by: Nie, Chenfei, et al.
Published: (2024)

Hardware-Efficient Compound IC Protection with Lightweight Cryptography
by: Aksoy, Levent, et al.
Published: (2026)

Privacy Preserving Machine Learning for Electric Vehicles: A Survey
by: Sani, Abdul Rahman, et al.
Published: (2022)

Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning
by: Scheu-Hachtel, Linda, et al.
Published: (2025)

Holding Secrets Accountable: Auditing Privacy-Preserving Machine Learning
by: Lycklama, Hidde, et al.
Published: (2024)

Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption
by: Njungle, Nges Brian, et al.
Published: (2025)

Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption
by: Guo, Yuqi, et al.
Published: (2024)

SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud
by: Duan, Shijin, et al.
Published: (2024)

A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2024)

Teaching an Old Dog New Tricks: Verifiable FHE Using Commodity Hardware
by: Drean, Jules, et al.
Published: (2024)

Blind Evaluation Framework for Fully Homomorphic Encryption and Privacy-Preserving Machine Learning
by: Lee, Hunjae "Timothy", et al.
Published: (2023)

Bicoptor 2.0: Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning
by: Zhou, Lijing, et al.
Published: (2023)

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
by: Zaredar, Farid, et al.
Published: (2025)

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment
by: Zaredar, Farid, et al.
Published: (2025)

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
by: Qin, Xian, et al.
Published: (2025)

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
by: Fotohi, Reza, et al.
Published: (2025)

Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks
by: Manh, Bui Duc, et al.
Published: (2024)

Privacy-Preserving Screening for Record Linkage
by: Huang, Chenyu, et al.
Published: (2026)

OnePath: Efficient and Privacy-Preserving Decision Tree Inference in the Cloud
by: Yuan, Shuai, et al.
Published: (2024)

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain
by: Wang, Weijie, et al.
Published: (2025)

Efficient Privacy-Preserving Approximation of the Kidney Exchange Problem
by: Breuer, Malte, et al.
Published: (2023)

GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
by: Frimpong, Eugene, et al.
Published: (2024)

Efficient and Lightweight In-memory Computing Architecture for Hardware Security
by: Ajmi, Hala, et al.
Published: (2022)

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption
by: Omar, Abdullah Al, et al.
Published: (2025)

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives
by: Zeng, Wenxuan, et al.
Published: (2025)

Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning
by: Bi, Yu, et al.
Published: (2024)

Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things
by: Bezanjani, Behnam Rezaei, et al.
Published: (2025)

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey
by: Zhang, Chaoyu, et al.
Published: (2024)

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2026)

Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
by: Damie, Marc, et al.
Published: (2025)

CURE: Privacy-Preserving Split Learning Done Right
by: Kanpak, Halil Ibrahim, et al.
Published: (2024)

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