4 citations · 11 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
Point Intervention: Improving ACVP Test Vector Generation Through Human Assisted Fuzzing
Iaroslav Gridin, Antonis Michalas
Automated Cryptographic Validation Protocol (ACVP) is an existing protocol that is used to validate a software or hardware cryptographic module automatically. In this work, we pres…
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…