A Secure and Verifiable Outsourcing Scheme for Assisting Mobile Device Training Machine Learning Model

Cheng Li,Li Yang,Jianfeng Ma,Ding Wang

doi:10.1155/2020/8825623

Abstract

In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.

Highlights

With the development of the Internet of Things, 5G communication networks, AI technology, and the construction of intelligent facilities that promote the development of mobile devices, connected cars, and smart wearable devices, concurrently, a large amount of data has been generated that is processed by different companies and servers
The contributions of this paper are as follows: (1) To solve the high calculation and high storage pressure caused by local machine learning algorithms on the device, the method called EVPP is proposed to outsource the computing part of the training process
When other nodes verify the security of the edge service node, the verification matrix is extracted from the evidence Eu→s, and the corresponding results are obtained according to the calculation rules to determine whether the evidence is valid

Summary

Introduction

With the development of the Internet of Things, 5G communication networks, AI technology, and the construction of intelligent facilities that promote the development of mobile devices, connected cars, and smart wearable devices, concurrently, a large amount of data has been generated that is processed by different companies and servers. (1) To solve the high calculation and high storage pressure caused by local machine learning algorithms on the device (especially mobile devices), the method called EVPP is proposed to outsource the computing part of the training process (2) To solve the problems of high latency and network transmission pressure in outsourced computing, a near-local outsourcing algorithm is proposed in conjunction with edge computing, and concurrently, a cryptographic device is designed to solve the privacy and security problems brought by data outsourcing; a random matrix calculation scheme is introduced to randomly perturb the calculation data (3) To prevent the dishonest outsourced computing nodes from affecting the training process, a trust mechanism with the arbitration function is proposed, which can guarantee the correctness of the calculation results of rational outsourced computing nodes. The scheme will be summarized, and future research directions will be discussed

Related Work

Problem Description and Research Goals

System Solutions

System Analysis

Discussion on the Application of the Scheme in Other Algorithms

Conclusion