Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

Training complex neural network models using third-party cloud-based infrastructure among
multiple data sources is a promising approach among existing machine learning solutions …

Motivated by the importance of floating-point computations, we study the problem of securely
and accurately summing many floating-point numbers. Prior work has focused on security …

Multiparty computation (MPC) is a cryptographic method that enables a set of parties to
compute an arbitrary joint function of the private inputs of all parties and does not reveal any …

Secure computation is the technology that computes an arbitrary function represented as a
circuit without revealing input values. Typical technologies related to secure computation are …

Multiparty computation (MPC) is a cryptographic method that enables a set of parties to
compute an arbitrary joint function of the private inputs of all parties and does not reveal any …

Secure multi-party computation (SMC) refers to the act of multiple participants jointly
computing an arbitrary function on private inputs without disclosing any information beyond …

Multiparty computation (MPC) is the technology that computes an arbitrary function
represented as a circuit without revealing input values. Typical MPC uses secret sharing …

Secure computation aims to compute a target function while keeping parties' input hidden
and outputting only the computation result. Secure computation has been attracting attention …

Each of a secure computation server apparatuses includes a random number generation
part that generates random numbers using a pseudo random number generator shared …