Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs)
use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal …

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …

We implement training of neural networks in secure multi-party computation (MPC) using
quantization commonly used in said setting. We are the first to present an MNIST classifier …

In this paper, we present the first Federated Learning (FL) framework which is secure
against both confidentiality and integrity threats from the aggregation server, in the case …

Since the first fully homomorphic encryption scheme was published in 2009, many papers
have been published on fully homomorphic encryption and its applications. Machine …

While the 5G is being rolled out in different industrial sectors, the 6G is expected to
implement data-driven ubiquitous machine learning for industrial information integration …

Gradient inversion attacks (GIAs) have posed significant challenges to the emerging
paradigm of distributed learning, which aims to reconstruct the private training data of clients …

Random forests have been one successful ensemble algorithms in machine learning.
Various techniques have been utilized to preserve the privacy of random forests from …