3 citations · 6 across the 4 of their papers we have counts for
4 papers
Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System
Shibam Mukherjee, Roman Walch, Fredrik Meisingseth +2
Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity…
Large-Scale MPC: Scaling Private Iris Code Uniqueness Checks to Millions of Users
Remco Bloemen, Bryan Gillespie, Daniel Kales +2
In this work we tackle privacy concerns in biometric verification systems that typically require server-side processing of sensitive data (e.g., fingerprints and Iris Codes). Concr…
CryptoTL: Private, Efficient and Secure Transfer Learning
Roman Walch, Samuel Sousa, Lukas Helminger +3
Big data has been a pervasive catchphrase in recent years, but dealing with data scarcity has become a crucial question for many real-world deep learning (DL) applications. A popul…
Privately Connecting Mobility to Infectious Diseases via Applied Cryptography
Alexandros Bampoulidis, Alessandro Bruni, Lukas Helminger +3
Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases. While pr…