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 …

Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …

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

In this paper, we propose a novel and practical mechanism which enables the service
provider to verify whether a suspect model is stolen from the victim model via model …

Deep learning models, especially those large-scale and high-performance ones, can be
very costly to train, demanding a considerable amount of data and computational resources …

Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN
models. Watermarking should be robust against watermark removal attacks that derive a …

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 …

Machine learning (ML) models are applied in an increasing variety of domains. The
availability of large amounts of data and computational resources encourages the …

Privacy-preserving machine learning (PPML) has many applications, from medical image
classification and anomaly detection to financial analysis. nGraph-HE enables data …