Enabling Efficient and Secure Outsourcing of Large Matrix Multiplications

Jia, Kun; Li, Hongwei; Liu, Dongxiao; Yu, Shui

doi:10.1109/glocom.2014.7417184

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…The traditional centralized computing architecture based on a cloud centre [3][4][5] has been unable to meet the requirements of modern devices and applications for low latency, high efficiency, and low cost applications. In some special scenarios, such as smart healthcare [6,7], identity recognition [9], smart homes [10], all have high requirements on time and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

Yang

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In smart applications, such as smart medical devices, in order to prevent privacy leaks, more data needs to be processed and trained locally or near the local end. 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), a machine learning method based on a collaboration of edge computing devices. 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, when a malicious service node is found, verifiable evidence can be generated to build a trust mechanism. 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. Keywords: Machine learning • Edge computing • Privacy-preserving • Mobile devices • Outsourced computingWe would like to thank the anonymous reviewers for their careful reading and useful comments.

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Section: Introductionmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

Yang

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

show abstract

“…The traditional centralized computing architecture based on a cloud center [3][4][5] has been unable to meet the requirements of modern devices and applications for low latency, high efficiency, and low-cost applications. In some special scenarios, such as smart healthcare [6,7] , identity recognition [8], and smart homes [9], all have high requirements on time and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

2020

Wireless Communications and Mobile Computing

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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.

show abstract

Enabling Efficient and Secure Outsourcing of Large Matrix Multiplications

Cited by 2 publications

References 15 publications

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

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

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