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 …

Training a Deep Learning (DL) model requires proprietary data and computing-intensive
resources. To recoup their training costs, a model provider can monetize DL models through …

The advent of the fourth industrial revolution along with developments in other emerging
technologies, such as Internet of Things, big data, artificial intelligence as well as cloud and …

Machine learning involves expensive data collection and training procedures. Model owners
may be concerned that valuable intellectual property can be leaked if adversaries mount …

The arms race between attacks and defenses for machine learning models has come to a
forefront in recent years, in both the security community and the privacy community …

Nowadays, due to the breakthrough in natural language generation (NLG), including
machine translation, document summarization, image captioning, etc NLG models have …

Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

The increasing adoption of machine learning inference in applications has led to a
corresponding increase in concerns about the privacy guarantees offered by existing …

Obtaining a well-trained model involves expensive data collection and training procedures,
therefore the model is a valuable intellectual property. Recent studies revealed that …