Cover Image

The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wei, Yu, Lu, Yun, Magdon-Ismail, Malik, Zikas, Vassilis
Format:	Preprint
Published:	2023
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

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

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

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

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
by: Wei, Wenjing, et al.
Published: (2026)

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)

A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers
by: Dani, Jimmy, et al.
Published: (2024)

Learning Robust and Privacy-Preserving Representations via Information Theory
by: Zhang, Binghui, et al.
Published: (2024)

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)

A Novel Review of Stability Techniques for Improved Privacy-Preserving Machine Learning
by: DuPlessie, Coleman, et al.
Published: (2024)

Sequential Auditing for f-Differential Privacy
by: Kutta, Tim, et al.
Published: (2026)

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

Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap
by: Zaman, Zakia, et al.
Published: (2026)

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

FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
by: Zeng, Zhihao, 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)

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
by: Saleem, Hamza, et al.
Published: (2024)

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

Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
by: Macias-Fassio, Eric, 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)

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

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

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

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

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

mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications
by: Monschein, David, et al.
Published: (2022)

Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
by: Ganadily, Naif A., et al.
Published: (2024)

Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning
by: Ju, Keyi, et al.
Published: (2023)

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
by: Chandrinos, Nikolaos, 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)

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

Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications
by: Jentsch, Julia, et al.
Published: (2024)

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

Privacy-Preserving Distributed Optimization and Learning
by: Chen, Ziqin, et al.
Published: (2024)

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

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

Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare
by: Nawaz, Anum, et al.
Published: (2025)