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