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

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

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 …