Most of our lives are conducted in the cyberspace. The human notion of privacy translates
into a cyber notion of privacy on many functions that take place in the cyberspace. This …

We consider the problem of massive matrix multiplication, which underlies many data
analytic applications, in a large-scale distributed system comprising a group of worker …

A distributed computing scenario is considered, where the computational power of a set of
worker nodes is used to perform a certain computation task over a dataset that is dispersed …

Recently large language models (LLMs) have gained significant attention as they have
shown surprising signs of artificial general intelligence (AGI). Artificial intelligence and large …

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the
goal is to allow a master to efficiently compute the pairwise products of two batches of …

We consider the problem of secure distributed matrix computation (SDMC), where a user
queries a function of data matrices generated at distributed source nodes. We assume the …

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user
wishes to compute the product of two matrices with the assistance of honest but curious …

We consider the problem of secure distributed matrix multiplication (SDMM), where a user
has two matrices and wishes to compute their product with the help of N honest but curious …

Large matrix multiplications commonly take place in large-scale machine-learning
applications. Often, the sheer size of these matrices prevent carrying out the multiplication at …

In this paper, we study the problem of private and secure distributed matrix multiplication
(PSDMM), where a user having a private matrix and non-colluding servers sharing a library …