Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push
the capabilities of AI to the network edge for real-time, efficient and secure intelligent …

Machine learning models are prone to memorizing sensitive data, making them vulnerable
to membership inference attacks in which an adversary aims to guess if an input sample was …

Machine learning models are vulnerable to membership inference attacks in which an
adversary aims to predict whether or not a particular sample was contained in the target …

Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Inference attacks against Machine Learning (ML) models allow adversaries to learn
sensitive information about training data, model parameters, etc. While researchers have …

Collaborative (federated) learning enables multiple parties to train a model without sharing
their private data, but through repeated sharing of the parameters of their local models …

Recently, recommender systems have achieved promising performances and become one
of the most widely used web applications. However, recommender systems are often trained …

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 …

Membership inference attacks are a key measure to evaluate privacy leakage in machine
learning (ML) models. It is important to train ML models that have high membership privacy …