Privacy-Preserving Cloud-Aided Broad Learning System

TL;DR

This paper introduces a secure and efficient cloud-based outsourcing method for Broad Learning System (BLS) that preserves data privacy and allows clients to verify result correctness, addressing resource constraints and security concerns.

Contribution

It presents a novel privacy-preserving outsourcing algorithm for BLS that enhances efficiency and enables result verification, a significant advancement over existing methods.

Findings

The algorithm ensures data privacy during outsourcing.

Clients can verify results with near certainty.

Experimental results confirm the algorithm's practicality and security.

Abstract

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.