4 citations · 11 across the 15 of their papers we have counts for
10 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…
Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning
Tanveer Khan, Mindaugas Budzys, Antonis Michalas
The popularity of Machine Learning (ML) makes the privacy of sensitive data more imperative than ever. Collaborative learning techniques like Split Learning (SL) aim to protect cli…
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…
Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation
Tanveer Khan, Antonis Michalas
Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption and implementation of cloud-based services. As a result, va…
Split Without a Leak: Reducing Privacy Leakage in Split Learning
Khoa Nguyen, Tanveer Khan, Antonis Michalas
The popularity of Deep Learning (DL) makes the privacy of sensitive data more imperative than ever. As a result, various privacy-preserving techniques have been implemented to pres…
Split Ways: Privacy-Preserving Training of Encrypted Data Using 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…
Blind Faith: Privacy-Preserving Machine Learning using Function Approximation
Tanveer Khan, Alexandros Bakas, Antonis Michalas
Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption of cloud-based services. As a result, various solutions hav…