International audienceThis paper presents a secure multiparty computation protocol for the Strassen-Winograd matrix multiplication algorithm. We focus on the setting in which any given player knows only one row (or one block of rows) of both input matrices and learns the corresponding row (or block of rows) of the resulting product matrix. Neither the player initial data, nor the intermediate values, even during the recurrence part of the algorithm, are ever revealed to other players. We use a combination of partial homomorphic encryption schemes and additive masking techniques together with a novel schedule for the location and encryption layout of all intermediate computations to preserve privacy. Compared to state of the art protocols, t...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
Secure Multi-Party Computation (MPC) is a concept that includes a system of n participants communica...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
International audienceThis paper presents a secure multiparty computation protocol for the Strassen-...
International audienceMatrix multiplication is a mathematical brick for solving many real life probl...
Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Comput...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
International audienceMapReduce is one of the most popular distributed programming paradigms that al...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
This document presents two novel techniques for Multi-Party Computation based on secret sharing wher...
International audienceThe MapReduce programming paradigm allows to process big data sets in parallel...
Abstract. Secure multi-party computation (MPC) allows a set of n players to securely compute an agre...
Secure multiparty computation is a basic concept of growing interest in modern cryptography. It allo...
Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over t...
We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
Secure Multi-Party Computation (MPC) is a concept that includes a system of n participants communica...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
International audienceThis paper presents a secure multiparty computation protocol for the Strassen-...
International audienceMatrix multiplication is a mathematical brick for solving many real life probl...
Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Comput...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
International audienceMapReduce is one of the most popular distributed programming paradigms that al...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
This document presents two novel techniques for Multi-Party Computation based on secret sharing wher...
International audienceThe MapReduce programming paradigm allows to process big data sets in parallel...
Abstract. Secure multi-party computation (MPC) allows a set of n players to securely compute an agre...
Secure multiparty computation is a basic concept of growing interest in modern cryptography. It allo...
Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over t...
We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
Secure Multi-Party Computation (MPC) is a concept that includes a system of n participants communica...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...