  We introduce a variation of coded computation that ensures data security and
master's privacy against workers, which is referred to as private secure coded
computation. In private secure coded computation, the master needs to compute a
function of its own dataset and one of the datasets in a library exclusively
shared by external workers. After recovering the desired function, the workers
must not be able to know which dataset in the library was desired by the master
or obtain any information about the master's own data. We propose a private
secure coded computation scheme for matrix multiplication, namely private
secure polynomial codes, based on private polynomial codes for private coded
computation. In simulations, we show that the private secure polynomial codes
achieves better computation time than private polynomial codes modified for
private secure coded computation.
