Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …

In recent years, machine learning algorithms, and more specifically deep learning
algorithms, have been widely used in many fields, including cyber security. However …

Most current approaches for protecting privacy in machine learning (ML) assume that
models exist in a vacuum. Yet, in reality, these models are part of larger systems that include …

Recently, a new paradigm of building general-purpose language models (eg, Google's Bert
and OpenAI's GPT-2) in Natural Language Processing (NLP) for text feature extraction, a …

Recent advancements in Deep Neural Networks (DNNs) have enabled widespread
deployment in multiple security-sensitive domains. The need for resource-intensive training …

The state-of-the-art performance of deep learning models comes at a high cost for
companies and institutions, due to the tedious data collection and the heavy processing …

Healthcare institutions are progressively integrating artificial intelligence (AI) into their
operations. The extraordinary potential of AI is restricted by insufficient medical data for AI …

Graph embeddings have been proposed to map graph data to low dimensional space for
downstream processing (eg, node classification or link prediction). With the increasing …

Deep Neural Network (DNN) models become one of the most valuable enterprise assets
due to their critical roles in all aspects of applications. With the trend of privatization …

AI assistants are becoming an integral part of society, used for asking advice or help in
personal and confidential issues. In this paper, we unveil a novel side-channel that can be …