Privacy-Preserving Cloud-Aided Broad Learning System
This work provides a privacy-preserving and verifiable solution for resource-constrained devices to leverage cloud computing for BLS training, which is an incremental improvement for users of BLS.
This paper addresses the computational burden of Broad Learning System (BLS) training for resource-constrained devices by proposing a cloud-aided outsourcing algorithm. The algorithm ensures data privacy and allows clients to verify the correctness of results with near-certainty, while improving client-side efficiency.
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.