Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Machine learning (ML) techniques have been widely used in many smart city sectors, where
a huge amount of data is gathered from various (IoT) devices. As a typical ML model …

Secure multi-party computation (MPC) allows a group of mutually distrustful parties to
compute a joint function on their inputs without revealing any information beyond the result …

A request from a client is received to generate a differentially private random forest classifier
trained using a set of restricted data. The differentially private random forest classifier is …

Many organizations wish to collaboratively train machine learning models on their combined
datasets for a common benefit (eg, better medical research, or fraud detection). However …

We propose privacy-preserving protocols for computing linear regression models, in the
setting where the training dataset is vertically distributed among several parties. Our main …

Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin,
the leading cryptocurrency, reached all-time highs many times over the year leading to …

We implement training of neural networks in secure multi-party computation (MPC) using
quantization commonly used in said setting. We are the first to present an MNIST classifier …

Contemporary biomedical databases include a wide range of information types from various
observational and instrumental sources. Among the most important features that unite …

In recent years, the extensive application of machine learning technologies has been
witnessed in various fields. However, in many applications, massive data are distributively …