面向机器学习的安全外包计算研究进展

陈珍珠; 周纯毅; 苏铓; 高艳松; 付安民

doi:10.7544/issn1000-1239.202220767

面向机器学习的安全外包计算研究进展

陈珍珠^{1, 2,},
周纯毅¹,
苏铓¹,
高艳松¹,
付安民^{1, 2, ,}

1.
南京理工大学计算机科学与工程学院　南京　210094
2.
广西可信软件重点实验室（桂林电子科技大学）广西桂林　541004

基金项目: 国家自然科学基金项目（62072239，62002167）；江苏省自然科学基金项目（BK20211192，BK20200461）；广西可信软件重点实验室研究课题（KX202029）

详细信息

作者简介:
陈珍珠: 1993年生. 博士研究生. 主要研究方向为云计算安全与机器学习安全

周纯毅: 1995年生. 博士研究生. 主要研究方向为机器学习安全与隐私保护

苏铓: 1987年生. 博士，副教授. 主要研究方向为安全访问控制与权限管理

高艳松: 1986年生. 博士，副教授. 主要研究方向为硬件安全、人工智能安全和隐私、系统安全

付安民: 1981年生. 博士，教授，博士生导师. CCF高级会员. 主要研究方向为密码学以及隐私保护

通讯作者:
付安民（fuam@njust.edu.cn）

中图分类号: TP391
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出版历程
- 收稿日期: 2022-08-28
- 修回日期: 2023-01-17
- 网络出版日期: 2023-04-17
- 刊出日期: 2023-06-30

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）.

More Information

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

摘要

摘要:
依靠机器学习，传统产业的数字化转型带来了海量数据增长，而产品服务的智能化提升则刺激了算力需求. 云计算的灵活资源调配可以为资源有限的企业和用户提供便宜便捷的外包计算服务，实现机器学习的模型训练和模型托管，加快产品和服务的智能化建设，促进数字经济增长. 然而，数据和模型外包伴随控制权转移，可能带来数据泄露风险和计算安全问题. 近年来，机器学习的外包安全问题受到越来越多研究者的关注，并取得了一些显著成果. 通过对2018—2022年这5年国内外机器学习安全外包研究工作调研，首先对现有主流的外包模型进行分类和特征归纳，依据任务阶段将外包模型划分为模型训练和模型托管模式，以及依据云服务商数量将外包模式划分为单云模式和多云模式. 其次重点从逻辑回归、朴素贝叶斯分类、支持向量机、决策树和神经网络等典型机器学习算法角度对机器学习安全外包计算相关研究进展进行了深入阐述和分析. 最后从不同角度分析和讨论了目前机器学习安全外包研究存在的不足，并展望未来面临的挑战和机遇.
- 云计算 /
- 外包计算 /
- 机器学习 /
- 迁移学习 /
- 隐私保护
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

HTML全文

图 1 2018—2022年调研文献来源统计分析

Figure 1. Statistics analysis of research literature source from 2018 to 2022

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图 2 外包计算威胁模型

Figure 2. Threat model for outsourced computing

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图 3 安全外包计算通用模型

Figure 3. General model for secure outsourced computing

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图 4 按机器学习任务阶段分类的2种模式

Figure 4. Two modes classified by task phase of machine learning

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图 5 按云服务商数量分类的2种模式

Figure 5. Two modes classified by the number of cloud service providers

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图 6 机器学习安全外包研究模型统计

Figure 6. Model statistics of machine learning security outsourced research

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图 7 通过岭回归训练逻辑回归

Figure 7. Training logistic regression by ridge regression

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图 8 朴素贝叶斯分类模型托管框架

Figure 8. Naive Bayes classification model hosting framework

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图 9 用户身份认证机制

Figure 9. User authentication mechanism

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图 10 决策树转换

Figure 10. Decision tree transformation

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图 11 双云模式下的DNN推理

Figure 11. DNN inference in two cloud modes

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表 1 机器学习外包计算模型的特点

Table 1 Features of Machine Learning Outsourced Computing Modes

模式应用趋势	特点
由模型训练到模型托管	支持多用户
	减少用户与云服务商交互
	支持密文托管
	支持用户离线
	支持模型机密性保护
	考虑半可信云服务器威胁
由单云到多云	分摊计算，支持MPC协议
	减少用户与云服务商交互
	支持用户离线
	增加恶意云服务器的威胁

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表 2 逻辑回归外包方案对比

Table 2 Comparison of Logistic Regression Outsourced Schemes

来源	单云/多云	外包阶段	加密工具	Sigmoid函数逼近	威胁模型	支持SIMD
文献[16]	单云	训练	近似FHE	最小二乘拟合多项式	半可信	√
文献[19]	多云	训练	层次HE，SGX	泰勒展开式	半可信	√
文献[20]	单云	托管	FHE	泰勒展开式	半可信	×
文献[21]	单云	训练	FHE	最小二乘拟合多项式	半可信	√
文献[22]	单云	训练	层次HE		半可信	√

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表 3 朴素贝叶斯分类外包方案对比

Table 3 Comparison of Naive Bayesian Classification Outsourced Schemes

来源	单云/多云	引入可信实体	外包阶段	加密工具	威胁模型	数据机密性	模型机密性	通信量
文献[24]	单云	×	推理	Paillier	半可信	×	×	高
文献[25]	单云	√	推理	Paillier	半可信	√	√	中
文献[26]	单云	×	推理	OU	恶意用户	√	√	低
文献[28]	单云	×	训练	Paillier	半可信	√	√	高
文献[31]	单云	√	训练+推理	Paillier	半可信	√	×	高

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表 4 支持向量机外包方案对比

Table 4 Comparison of Support Vector Machine Outsourced Schemes

来源	单云/多云	加密模型	引入可信实体	外包阶段	加密工具	威胁模型	结果可验证性	多次交互
文献[35]	单云	√	×	推理	Paillier+GC	半可信	×	√
文献[36]	多云	√	√	训练+推理	DT-PKC	半可信	×	×
文献[37]	多云	√	√	训练	DT-PKC	半可信	×	√
文献[38]	单云	×	×	推理	FHE	半可信	×	×
文献[40]	多云	√	√	推理	矩阵盲化	恶意云	√	√
文献[41]	单云	×	√	推理	DT-PKC / BGN	恶意用户	×	√

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表 5 决策树外包方案对比

Table 5 Comparison of Decision Tree Outsourced Schemes

来源	单云/多云	加密模型	外包阶段	加密工具	威胁模型	支持随机森林	离线
文献[46]	单云	×	推理	HE+ GC+OT	半可信	×	×
文献[47]	单云	×	推理	GC, OT, ORAM	半可信	×	×
文献[49]	多云	√	推理	秘密共享	半可信	×	√
文献[50]	多云	√	训练+推理	FHE	半可信	√	√
文献[51]	多云	√	推理	GC+秘密共享	恶意用户	×	√
文献[52]	单云	√	推理	对称加密	半可信	×	√
文献[53]	单云	√	推理	HE	恶意用户	×	×
文献[54]	多云	√	推理	HE+秘密共享	半可信	×	√
文献[55]	多云	×	训练+推理	DT-PKC+秘密共享	半可信	×	√
文献[56]	单云	√	推理	多密钥HE+OT	半可信	√	×
文献[57]	单云	√	训练+推理	矩阵盲化	半可信	×	√
文献[58]	单云	√	推理	HE	半可信	×	√

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表 6 神经网络外包方案对比

Table 6 Comparison of Neural Network Outsourced Schemes

来源	算法	单云/多云	外包阶段	加密工具	威胁模型	可验证	模型隐私
文献[60]	SLP	单云	训练+推理	矩阵盲化	半可信	×	√
文献[61]	SLP	单云	训练+推理	矩阵盲化	恶意云	√	√
文献[62]	DNN	单云	训练+推理	DT-PKC	半可信	×	√
文献[63]	R-CNN	多云	训练	秘密共享	半可信	×	√
文献[64]	DNN	单云	训练	加噪	半可信	×	×
文献[65]	CNN	多云	训练	秘密共享	半可信	×	×
文献[66]	DNN	单云	训练	矩阵盲化	半可信	×	√
文献[67]	DNN	多云	推理	秘密共享	半可信	×	√
文献[68]	DNN	多云	推理	秘密共享	半可信	×	×
文献[69]	CNN	多云	推理	秘密共享	半可信	×	×
文献[70]	DNN	多云	推理	秘密共享	半可信	×	√
文献[71]	CNN	单云	推理	加性HE	半可信	×	×
文献[72]	DNN	单云	推理	HE+ GC+秘密共享	半可信	×	√
文献[73]	CNN	单云	推理	HE+秘密共享	半可信	×	√
文献[74]	CNN	单云	推理	HE+ GC+秘密共享	恶意用户	×	√
文献[75]	CNN	多云	推理	GC+秘密共享	半可信	×	√
文献[76]	DNN	多云	推理	GC	恶意云	×	√
文献[77]	DNN	多云	推理	HE+秘密共享	半可信	×	√
文献[78]	DNN	多云	训练+推理	GC+秘密共享	半可信	×	√
文献[79−80]	DNN	多云	推理	秘密共享	半可信	×	√
文献[81−84]	CNN	多云	推理	GC+秘密共享	恶意云	×	√

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