Research Progress of Secure Outsourced Computing for Machine Learning

Chen Zhenzhu; Zhou Chunyi; Su Mang; Gao Yansong; Fu Anmin

doi:10.7544/issn1000-1239.202220767

Journal of Computer Research and Development > 2023 > 60(7): 1450-1466. > DOI: 10.7544/issn1000-1239.202220767

Chen Zhenzhu, Zhou Chunyi, Su Mang, Gao Yansong, Fu Anmin. Research Progress of Secure Outsourced Computing for Machine Learning[J]. Journal of Computer Research and Development, 2023, 60(7): 1450-1466. DOI: 10.7544/issn1000-1239.202220767

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Research Progress of Secure Outsourced Computing for Machine Learning

Chen Zhenzhu^{1, 2,},
Zhou Chunyi¹,
Su Mang¹,
Gao Yansong¹,
Fu Anmin^{1, 2, ,}

1.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094
2.
Guangxi Key Laboratory of Trusted Software(Guilin University of Electronic Technology), Guilin, Guangxi 541004

Funds: This work was supported by the National Natural Science Foundation of China（62072239, 62002167），the Natural Science Foundation of Jiangsu Province（BK20211192, BK20200461），and the Project of Guangxi Key Laboratory of Trusted Software（KX202029）.

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Author Bio:
Chen Zhenzhu: born in 1993. PhD candidate. Her main research interest includes cloud computing security and machine learning security

Zhou Chunyi: born in 1995. PhD candidate. His main research interest includes machine learning security and privacy preserving

Su Mang: born in 1987. PhD, associate professor. Her main research interests include secure access control and right management

Gao Yansong: born in 1986. PhD, associate professor. His current research interests include hardware security, AI security and privacy, and system security

Fu Anmin: born in 1981. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include cryptography and privacy preserving
Received Date: August 28, 2022
Revised Date: January 17, 2023
Available Online: April 17, 2023

Graphical Abstract

Abstract

Abstract

Based on machine learning, the digital transformation of traditional industries brings a massive data growth, while the intelligent enhancement of products services raises the demand for computing power. Cloud computing, relying on flexible resource deployment, can provide inexpensive and convenient outsourced computing services for users with limited resources, enabling them to complete model training and model hosting for machine learning. It also contributes to the intelligent improvement of products and services and promotes the growth of the digital economy. However, data and model outsourcing come with a transfer of control, which may pose data leakage risk and computational security issues. In recent years, the security issues of machine learning outsourcing have received increasing public attentions and academic concerns. In this paper, we systematically reviewed the research work on machine learning security outsourcing in the year of 2018−2022 the past five years. We first present different outsourced modes, including model training and model hosting modes classified by the task phase, single-cloud and multi-cloud modes classified by the number of cloud service providers. Then we summarize the characteristics of outsourced models under different modes. Next, we focus on the research progress related to machine learning secure outsourced computing from the perspective of typical machine learning algorithms such as logistic regression, Bayesian classification, support vector machine, decision tree and neural network, and provide an in-depth description and analysis. Finally, we analyze and discuss the limitations from different perspectives, as well as potential challenges and opportunities.
- cloud computing,
- outsourced computing,
- machine learning,
- transfer learning,
- privacy preserving

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References (85)

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