Federated learning (FL) has recently emerged as a striking framework for allowing machine
and deep learning models with thousands of participants to have distributed training to …

Side-channel attacks have become a realistic threat to implementations of cryptographic
algorithms, especially with the help of deep-learning techniques. The majority of recently …

Several attacks on AES using far field electromagnetic (EM) emission as a side channel
have been recently presented. Unlike power analysis or near filed EM analysis, far field EM …

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective,
yet its vulnerability to attacks that extract information from model weights is widely …

In federated learning (FL), in contrast to centralized ML learning processes, ML models are
sent rather than the raw data. Therefore, FL is a decentralized and privacy-compliant …

Under stringent privacy constraints, whether federated recommendation systems can
achieve group fairness remains an inadequately explored question. Taking gender fairness …

The majority of recently demonstrated Deep-Learning Side-Channel Attacks (DLSCAs) use
neural networks trained on a segment of traces containing operations only related to the …

The majority of recently demonstrated Deep-Learning Side-Channel Attacks (DLSCAs) use
neural networks trained on a segment of traces containing operations only related to the …

Abstract Systems that use secret keys or personal details are seriously at risk from side-
channel attacks, especially if they rely on power analysis. Attackers can use unintentional …

In this paper, we focus on the problem of Transfer Learning (TL) in Side-Channel Analysis
(SCA) due to differences between the source device and the target device. Such differences …