Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Naive Bayes (NB) is a simple but highly practical classifier, with a wide range of applications
including spam filters, cancer diagnosis and face recognition, to name a few examples only …

Many data-driven personalized services require that private data of users is scored against a
trained machine learning model. In this paper we propose a novel protocol for privacy …

Background In biomedical applications, valuable data is often split between owners who
cannot openly share the data because of privacy regulations and concerns. Training …

Classification of personal text messages has many useful applications in surveillance, e-
commerce, and mental health care, to name a few. Giving applications access to personal …

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely
on the use of large amounts of personal data for training and inference. Among the most …

Neural network is a particular machine learning framework which has gained widespread
popularity due to its superior performance in many applications, such as complex board …

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE),
support a number of useful computations on ciphertext in a broad range of applications …

Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving
training of decision trees over distributed data assume that the features are categorical. In …

We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of
private text classification. In this setting, a party (Alice) holds a text message, while another …