Privacy-Preserving cloud-Aided broad learning system

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. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm enables resource constrained devices to outsource BLS algorithm to untrusted cloud server to complete model training, which is of great significance for the promotion and applica-tion of BLS algorithm. Compared with the original BLS algorithm, this algorithm not only improves the efficiency of the algorithm on the client, but also ensures that the sensitive information of the client will not be 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The paper introduces a secure, efficient, and verifiable outsourcing algorithm for BLS, allowing resource-constrained devices to outsource BLS algorithm to cloud server for model training while ensuring the confidentiality of client's sensitive information. It also enables client to verify the correctness of returned results with a high probability.