Gradient leakage attacks are considered one of the wickedest privacy threats in deep
learning as attackers covertly spy gradient updates during iterative training without …

Abstract Machine Learning as a Service (MLaaS) has significantly advanced data-driven
decision-making and the development of intelligent applications. However, the privacy risks …

Transfer learning, successful in knowledge translation across related tasks, faces a
substantial privacy threat from membership inference attacks (MIAs). These attacks, despite …

Federated learning (FL) enables collaborative model training without sharing private data,
thereby potentially meeting the growing demand for data privacy protection. Despite its …

Abstract Model extraction attacks are considered to be a significant avenue of vulnerability in
machine learning. In model extraction attacks, the attacker repeatedly queries a victim model …

The rapid growth of IoT technology has heightened the requirement for effective data
management and analysis. Knowledge graphs (KGs) and large pre-trained language …

Membership Inference Attacks (MIA) are considered the fundamental privacy risk in Machine
Learning (ML), which attempt to determine whether a specific data sample is training data for …