activity
20232025
most citedWildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training

1 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2025

A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption

Khoa Nguyen, Tanveer Khan, Hossein Abdinasibfar +1

Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces…

cs.CR2024★ 1 cited

A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption

Khoa Nguyen, Mindaugas Budzys, Eugene Frimpong +2

Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks…

cs.CR2024★ 1 cited

Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training

Tanveer Khan, Mindaugas Budzys, Khoa Nguyen +1

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial comp…

cs.LG2024★ 1 cited

GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption

Eugene Frimpong, Khoa Nguyen, Mindaugas Budzys +2

Machine Learning (ML) has emerged as one of data science's most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns o…

cs.CR2023

Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption

Tanveer Khan, Khoa Nguyen, Antonis Michalas +1

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw…

cs.CR2023

A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning

Tanveer Khan, Khoa Nguyen, Antonis Michalas

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw…