Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Machine learning (ML) has become a core component of many real-world applications and
training data is a key factor that drives current progress. This huge success has led Internet …

Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s
to a tool for building real systems today. Over the past decade, MPC has been one of the …

Consider two data providers, each maintaining private records of different feature sets about
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …

Machine learning (ML) has progressed rapidly during the past decade and the major factor
that drives such development is the unprecedented large-scale data. As data generation is a …

Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted
data. Ló pez-Alt et al.(STOC 2012) proposed a generalized notion of HE, called Multi-Key …

In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …

SPDZ denotes a multiparty computation scheme in the preprocessing model based on
somewhat homomorphic encryption (SHE) in the form of BGV. At CCS'16, Keller et al …