Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

Machine learning (ML) has been widely recognized as an enabler of the global trend of
digital transformation. With the increasing adoption of data-hungry machine learning …

Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced
federated learning (FL), which enables the server to obtain the sum of users' locally trained …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

Deep learning (DL) has demonstrated superior success in various of applications, such as
image classification, speech recognition, and anomalous detection. The unprecedented …

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal
of a client's influence from the global model in Federated Learning (FL) systems, thereby …

Abstract Privacy-Preserving Deep Learning (PPDL) has been successfully applied in the
inference phase to preserve the privacy of input data. However, PPDL models are …

Multi-party training frameworks for decision trees based on secure multi-party computation
enable multiple parties to train high-performance models on distributed private data with …

Traditional secure multiparty computation (MPC) protocols presuppose a fixed set of
participants throughout the computational process. To address this limitation, Fluid MPC …

The Multi-party Secure Computation (MPC)-based methods for privacy-preserving Graph
Neural Networks (GNNs) are still challenged by high communication overhead. Moreover …