  The Gram matrix of a matrix $A$ is defined as $AA^T$ (or $A^T\!A$). 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 the product of two matrices using
the help of worker servers. If a Gram matrix were computed using SDMM, the data
matrix would need to be encoded twice, which causes an unnecessary overhead in
the communication cost. We propose a new scheme for this purpose called secure
distributed Gram matrix multiplication (SDGMM). It can leverage the advantages
of computing a Gram matrix instead of a regular matrix product.
