Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …

Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial
intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual …

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for
mobile systems to limit privacy leakages in federated learning. Leveraging the widespread …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

Machine learning (ML) has become a core component of many real-world applications and
training data is a key factor that drives current progress. This huge success has led Internet …

Machine learning models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

The prevalence of deep learning has drawn attention to the privacy protection of sensitive
data. Various privacy threats have been presented, where an adversary can steal model …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …

We present DarkneTZ, a framework that uses an edge device's Trusted Execution
Environment (TEE) in conjunction with model partitioning to limit the attack surface against …

Security of machine learning is increasingly becoming a major concern due to the
ubiquitous deployment of deep learning in many security-sensitive domains. Many prior …