4 citations · 11 across the 15 of their papers we have counts for
18 papers
Oops!... They Stole it Again: Attacks on Split Learning
Tanveer Khan, Antonis Michalas
Split Learning (SL) is a collaborative learning approach that improves privacy by keeping data on the client-side while sharing only the intermediate output with a server. However,…
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…
Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing
Tanveer Khan, Mindaugas Budzys, Antonis Michalas
Split Learning (SL) -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning (ML) processes. Though promising, SL has proven vulnerabl…
To Vaccinate or not to Vaccinate? Analyzing Power over the Pandemic
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creation of vaccines. To halt the COVID…
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…
Trustworthiness of Users: A One-Class Classification Approach
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
(formerly Twitter) is a prominent online social media platform that plays an important role in sharing information making the content generated on this platform a valu…
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…
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…
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users
Tanveer Khan, Antonis Michalas
Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, they a…
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…
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users
Tanveer Khan, Antonis Michalas
Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there…
SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?
Tanveer Khan, Antonis Michalas, Adnan Akhunzada
Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal…