Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

Rapid development of large-scale pre-training has resulted in foundation models that can
act as effective feature extractors on a variety of downstream tasks and domains. Motivated …

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without
decrypting it. FHE has garnered significant attention over the past decade as it supports …

Generative AI, including large language models (LLMs), has transformed the paradigm of
data generation and creative content, but this progress raises critical privacy concerns …

The benefits of new technology are becoming increasingly apparent to organisations as
digital transformation continues. However, as technology becomes more widely used …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …

Mobile devices have become an essential element in our day-to-day lives. The chances of
mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting …

Data-driven industrial health prognostics require rich training data to develop accurate and
reliable predictive models. However, stringent data privacy laws and the abundance of edge …