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 …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that
need to be addressed to ensure secure and trustworthy socio-technical infrastructures …

Deep learning has been used to address various problems in a range of domains within
both academia and industry. However, the issue of intellectual property with deep learning …

Due to their capacity for image generation, GAN models may be considered as a solution for
the use of private data, which enhances their commercial value. However, unlike …

In the rapidly evolving landscape of cybersecurity, model extraction attacks pose a
significant challenge, undermining the integrity of machine learning models by enabling …

Adversarial attacks have threatened the credibility of machine learning models and cast
doubts over the integrity of data. The attacks have created much harm in the fields of …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

Modern Autonomous Vehicles (AVs) leverage road context information collected through
sensors (eg, LiDAR, radar, and camera) to support the automated driving experience. Once …

For many years, car keys have been the sole mean of authentication in vehicles. Whether
the access control process is physical or wireless, entrusting the ownership of a vehicle to a …