Protection of access to a tensor in outsourcing deep learning computations via shuffling. For example, the tensor in the computation of an artificial neural network can be partitioned into portions of different sizes. The computing tasks can be generated for operating on the portions such that the results of the computing tasks can be combined to obtain the result of a computing task operates on the tensor in the computation of the artificial neural network. The computing tasks can be shuffled for distribution out of order to external entities. The partitioning and shuffling can prevent the external entities from accessing and/or reconstructing the tensor.