1 citations · 3 across the 6 of their papers we have counts for
6 papers
Hidden Elo: Private Matchmaking through Encrypted Rating Systems
Mindaugas Budzys, Bin Liu, Antonis Michalas
Matchmaking has become a prevalent part in contemporary applications, being used in dating apps, social media, online games, contact tracing and in various other use-cases. However…
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…
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…
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…