  We consider the problem of evaluating distinct multivariate polynomials over
several massive datasets in a distributed computing system with a single master
node and multiple worker nodes. We focus on the general case when each
multivariate polynomial is evaluated over its corresponding dataset and propose
a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to
perform all computations simultaneously while providing robustness against
stragglers who do not respond in time, adversarial workers who respond with
wrong computation and information-theoretic security of dataset against
colluding workers. Our scheme introduces a small computation overhead which
results in a reduction in download cost and also offers comparable resistance
to stragglers over existing solutions. On top of it, we also propose two
verification schemes to detect the presence of adversaries, which leads to
incorrect results, without involving additional nodes.
