privacy

Scaling Private Iris Code Uniqueness Checks to Millions of Users

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). Concretely, we design a solution that allows us to query whether a given …

Scaling Private Iris Code Uniqueness Checks to Millions of Users

Large-Scale MPC: Scaling Private Iris Code Uniqueness Checks to Millions of Users

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). Concretely, we design a solution that allows us to query whether a given …

CryptoTL: Private, efficient and secure transfer learning

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 popular methodology to efficiently enable the training of DL models to …

Privacy-Preserving Machine Learning Using Cryptography

Data scientists require an extensive training set to train an accurate and reliable machine learning model – the bigger and diverse the training set, the better. However, acquiring such a vast training set can be difficult, especially when sensitive …