As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

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 …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Differential privacy is a strong notion for privacy that can be used to prove formal
guarantees, in terms of a privacy budget, ε, about how much information is leaked by a …

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 …

This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

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

Hardware security and trust have become a pressing issue during the last two decades due
to the globalization of the semiconductor supply chain and ubiquitous network connection of …

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 …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …