1 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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…
MetaPriv: Acting in Favor of Privacy on Social Media Platforms
Robert Cantaragiu, Antonis Michalas, Eugene Frimpong +1
Social networks such as Facebook (FB) and Instagram are known for tracking user online behaviour for commercial gain. To this day, there is practically no other way of achieving pr…