Okko Makkonen

Aalto University · Department of Mathematics and Systems Analysis

In this paper, we propose a novel construction for secure distributed matrix multiplication (SDMM) based on algebraic geometry (AG) codes. The proposed construction is inspired by the GASP code, where so-called gaps in a certain polynomial are utilized to achieve higher communication rates. Our construction considers the gaps in a Weierstrass semig...

The Gram matrix of a matrix $A$ is defined as $AA^T$ (or $A^TA$). Computing the Gram matrix is an important operation in many applications, such as linear regression with the least squares method, where the explicit solution formula includes the Gram matrix of the data matrix. Secure distributed matrix multiplication (SDMM) can be used to compute t...

In this paper, a general framework for linear secure distributed matrix multiplication (SDMM) is introduced. The model allows for a neat treatment of straggling and Byzantine servers via a star product interpretation as well as simplified security proofs. Known properties of star products also immediately yield a lower bound for the recovery thresh...

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...

This work considers the problem of privately outsourcing the computation of a matrix product over a finite field ${\mathbb {F}}_{q}$ to $N$ helper servers. These servers are considered to be honest but curious, i.e. , they behave according to the protocol but will try to deduce information about the user’s data. Furthermore, any set of up to...