Securely Straggler-Exploiting Coded Computation for Distributed Matrix Multiplication

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of slow workers, called stragglers, using a coding approach. We assume that the straggler computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. In this scenario, a new coded computation scheme is proposed to preserve the data security and privacy from workers, which is called
securely straggler-exploiting codes (SSEC)
. Moreover, the proposed SSEC can efficiently reduce the communication load in distributed computing for assigning the sub-tasks to the workers, by overlapping the encoded matrices in assigning multiple sub-tasks with appropriate polynomial functions. It is also proven that the data security and privacy constraints are satisfied in SSEC in an information-theoretic sense. In conclusion, SSEC shows good performance on the recovery threshold and communication loads and compare them with the existing secure coded computation schemes for matrix multiplication tasks.