Cover Image

Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Zhao, Dongfang
Format:	Preprint
Published:	2024
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

Securing Transfer-Learned Networks with Reverse Homomorphic Encryption
by: Allison, Robert, et al.
Published: (2025)

Efficient Pruning for Machine Learning Under Homomorphic Encryption
by: Aharoni, Ehud, et al.
Published: (2022)

Enhancing Data Provenance and Model Transparency in Federated Learning Systems -- A Database Approach
by: Gu, Michael, et al.
Published: (2024)

HEART: A High-Efficiency Adaptive Real-Time Telemonitoring Framework for Secure Electrocardiogram Signal Transmission Using Chaotic Encryption
by: Yuksel, Beyazıt Bestami
Published: (2026)

PRISM: Privacy-preserving Inference System with Homomorphic Encryption and Modular Activation
by: Elkhatib, Zeinab, et al.
Published: (2025)

A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2025)

Compile-Time Fully Homomorphic Encryption of Vectors: Eliminating Online Encryption via Algebraic Basis Synthesis
by: Zhao, Dongfang
Published: (2025)

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
by: Panzade, Prajwal, et al.
Published: (2022)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
by: Huckelberry, Jacob, et al.
Published: (2024)

On the (In)Security of Loading Machine Learning Models
by: Digregorio, Gabriele, et al.
Published: (2025)

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

Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference
by: Cheerla, Kalyan, et al.
Published: (2025)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
by: Ioniţă, Alexandru, et al.
Published: (2025)

Information Flow Control in Machine Learning through Modular Model Architecture
by: Tiwari, Trishita, et al.
Published: (2023)

CECILIA: Comprehensive Secure Machine Learning Framework
by: Ünal, Ali Burak, et al.
Published: (2022)

Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering
by: Effendi, Fabrianne, et al.
Published: (2024)

Exploiting ftrace's function_graph Tracer Features for Machine Learning: A Case Study on Encryption Detection
by: Begovic, Kenan, et al.
Published: (2025)

Learned-Database Systems Security
by: Schuster, Roei, et al.
Published: (2022)

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

Plinius: Secure and Persistent Machine Learning Model Training
by: Yuhala, Peterson, et al.
Published: (2021)

Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments
by: Agarwal, Dhruv Deepak, et al.
Published: (2025)

ACE: A Security Architecture for LLM-Integrated App Systems
by: Li, Evan, et al.
Published: (2025)

FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
by: Li, Yang, et al.
Published: (2023)

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
by: Jin, Weizhao, et al.
Published: (2023)

Machine Learning Driven Smishing Detection Framework for Mobile Security
by: Goel, Diksha, et al.
Published: (2024)

Covert Attacks on Machine Learning Training in Passively Secure MPC
by: Jagielski, Matthew, et al.
Published: (2025)

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
by: Angioni, Daniele, et al.
Published: (2024)

Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security
by: Esmaeili, Mona, et al.
Published: (2024)

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

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications
by: Haug, Markus, et al.
Published: (2025)

Securing Federated Learning against Backdoor Threats with Foundation Model Integration
by: Bi, Xiaohuan, et al.
Published: (2024)

HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
by: Lee, Seewoo, et al.
Published: (2024)

CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations
by: Sefsaf, Bilel, et al.
Published: (2026)

VLWE: Variety-based Learning with Errors for Vector Encryption through Algebraic Geometry
by: Zhao, Dongfang
Published: (2025)

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning
by: Hasan, Syed Mhamudul, et al.
Published: (2024)

AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
by: Takahashi, Hideaki
Published: (2023)

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025)

A System for Interactive Examination of Learned Security Policies
by: Hammar, Kim, et al.
Published: (2022)

Encryption-Friendly LLM Architecture
by: Rho, Donghwan, et al.
Published: (2024)

Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
by: Asghar, Hassan Jameel, et al.
Published: (2023)