  With the rapid development of artificial intelligence and the advent of the
5G era, deep learning has received extensive attention from researchers. Broad
Learning System (BLS) is a new deep learning model proposed recently, which
shows its effectiveness in many fields, such as image recognition and fault
detection. However, the training process still requires vast computations, and
therefore cannot be accomplished by some resource-constrained devices. To solve
this problem, the resource-constrained device can outsource the BLS algorithm
to cloud servers. Nevertheless, some security challenges also follow with the
use of cloud computing, including the privacy of the data and the correctness
of returned results. In this paper, we propose a secure, efficient, and
verifiable outsourcing algorithm for BLS. This algorithm not only improves the
efficiency of the algorithm on the client but also ensures that the clients
sensitive information is not leaked to the cloud server. In addition, in our
algorithm, the client can verify the correctness of returned results with a
probability of almost 1. Finally, we analyze the security and efficiency of our
algorithm in theory and prove our algorithms feasibility through experiments.
