This work considers the problem of distributing matrix multiplication over the real or complex numbers to helper servers, such that the information leakage to these servers is close to being information-theoretically secure. These servers are assumed to be honest-but-curious, i.e., they work according to the protocol, but try to deduce information about the data. The problem of secure distributed matrix multiplication (SDMM) has been considered in the context of matrix multiplication over finite fields, which is not always feasible in real world applications. We present two schemes, which allow for variable degree of security based on the use case and allow for colluding and straggling servers. We analyze the security and the numerical accu...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
International audienceWith the emergence of cloud computing services, computationally weak devices (...
International audienceThe MapReduce programming paradigm allows to process big data sets in parallel...
Funding Information: This work has been supported by the Academy of Finland, under Grants No. 318937...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
Funding Information: C. Hollanti and J. Li were supported by the Academy of Finland, under Grants No...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
International audienceMapReduce is one of the most popular distributed programming paradigms that al...
We consider the problem of secure distributed matrix multiplication (SDMM), where a user has two mat...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Matrix Multiplication is a basic engineering and scientific problem, which has application in variou...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
International audienceWith the emergence of cloud computing services, computationally weak devices (...
International audienceThe MapReduce programming paradigm allows to process big data sets in parallel...
Funding Information: This work has been supported by the Academy of Finland, under Grants No. 318937...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix...
Funding Information: C. Hollanti and J. Li were supported by the Academy of Finland, under Grants No...
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a func...
International audienceMapReduce is one of the most popular distributed programming paradigms that al...
We consider the problem of secure distributed matrix multiplication (SDMM), where a user has two mat...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has be...
Large matrix multiplications commonly take place in large-scale machine-learning applications. Often...
The distributed matrix multiplication problem with an unknown number of stragglers is considered, wh...
Matrix Multiplication is a basic engineering and scientific problem, which has application in variou...
International audienceThis paper deals with distributed matrix multiplication. Each player owns only...
International audienceWith the emergence of cloud computing services, computationally weak devices (...
International audienceThe MapReduce programming paradigm allows to process big data sets in parallel...