纵向联邦学习方法及其隐私和安全综述

doi:10.11959/j.issn.2096-109x.2023017

Abstract

Abstract:

Federated learning （FL） is a distributed machine learning technology that enables joint construction of machine learning models by transmitting intermediate results （e.g., model parameters, parameter gradients, embedding representation, etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage, since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions, FL is usually divided into horizontal federated learning （HFL）, vertical federated learning （VFL）, and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis, financial and security of VFL.Although VFL performs well in real-world applications, it still faces many privacy and security challenges.To the best of our knowledge, no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework, communication mechanism, alignment mechanism, and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally, the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems, the future direction and development trend of VFL were outlined, to provide a reference for the theoretical research of building an efficient, robust and safe VFL.

Key words: vertical federated learning, security and privacy, backdoor attack, inference attack and defense, adversarial attack, security evaluation

CLC Number:

TP18

Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG. Survey on vertical federated learning: algorithm, privacy and security[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 1-20.

Figures/Tables 6

References 106

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攻击类型	攻击目标	攻击方法	攻击阶段			攻击背景知识		威胁强度	VFL框架	数据模态
攻击类型	攻击目标	攻击方法	训练阶段	推理阶段	协调者模型	嵌入信息	标签信息	威胁强度	VFL框架	数据模态
隐私性	标签	模型补全	√	√	×	×	×	●	神经网络	图像、文本
隐私性	标签	主动攻击	√	×	×	×	×	●	神经网络	图像、文本
隐私性	标签	直接攻击	√	√	×	×	×	○	神经网络	图像、文本
隐私性	标签	范数攻击	√	√	×	√	×	○	神经网络	文本
隐私性	属性	等式求解攻击	√	√	×	×	×	?	逻辑回归	文本
隐私性	属性	路径限制攻击	√	√	×	×	×	?	决策树	文本
隐私性	属性	生成回归网络	×	√	√	√	×	●	神经网络	文本
隐私性	属性	解码器攻击	√	√	×	√	×	●	神经网络	图像、文本
隐私性	成员信息	PSI暴露	√	√	×	×	×	?	神经网络、逻辑回归	图像、文本
隐私性	原始数据	逆向乘法攻击	√	×	×	×	×	?	逻辑回归	文本
隐私性	原始数据	逆向加法攻击^]	√	×	×	×	×	?	XGBoost	文本
隐私性	原始数据	特征空间劫持攻击	√	×	×	×	×	●	神经网络	图像
安全性	VFL模型	梯度替换后门攻击	√	×	×	×	√	●	神经网络	图像、文本
注：√表示满足，×表示不满足。威胁强度（根据攻击所需背景知识和攻击场景评估其具有的威胁性）：○表示低，?表示中，●表示高。

攻击发起者	背景知识					操纵能力
攻击发起者	中间嵌入	更新梯度	标签信息	边缘模型	本地属性	中间嵌入	更新梯度	标签信息	边缘模型	本地属性
主参与者	√	√	√	√	√	√	√	√	√	√
从参与者	√	√	×	√	√	×	×	×	√	√

类型	横向联邦学习	纵向联邦学习	风险独特性
数据提供	每个参与者都有各自的特征和标签	主动方具有标签，被动方具有特征	易遭受标签推断攻击
数据处理	无须对齐操作	需要进行数据对齐	数据成员直接在对齐过程中被泄露
模型结构	参与者模型结构完整	参与者模型结构不完整	攻击者对全局模型的操纵能力相对较弱
传输内容	上传下发模型参数信息或梯度信息	上传嵌入层信息，下发梯度信息	嵌入层成为隐私泄露或者攻击的目标
聚合机制	平均聚合等鲁棒聚合方式	直接拼接或相加	缺少对后门攻击的聚合防御
推理方式	参与者对全局模型单独推理，获得不同推理结果	参与者需要联合进行推理，输出同一个推理结果	易在推理阶段引入对抗攻击

适用场景	数据集	样本数量	特征数量	标签类别	数据模态	任务类型	VFL引用文献
	MIMIC-III^[81]	21 139	714	2	文本	分类任务	[27],[28],[29]
生物医疗	Breast^[82]	569	31	2	文本	分类任务	[16],[21],[75],[100]
	Purchase100^[51]	197 324	600	2	文本	分类任务	[51]
	Insurance Claim^[33]	188 318	116	2	文本	分类任务	[34]
金融交易	Bank Market^[83]	4 521	17	2	文本	分类任务	[20],[101]
	Credit Card Clients^[84]	284 807	28	2	文本	分类任务	[17],[27],[31],[34],[49],[75],[100],[102]
	Adult^[85]	48 842	12	2	文本	分类任务	[51],[75]
	Criteo^[86]	4 500 000	40	2	文本	分类任务	[21],[103]
广告推荐	Avazu	4 000 000	17	2	文本	分类任务	[103]
	Cora^[87]	2 708	1 433	7	网络	分类任务	[22],[23],[104]
文献引用	Citeseer^[87]	3 312	3 703	6	网络	分类任务	[22],[23],[104]
	Pubmed^[88]	19 717	500	3	网络	分类任务	[22],[23],[104]
	MNIST^[89]	70 000	784	10	图像	分类任务	[16],[18],[27],[28],[29][31],[45],[105],[106]
	Fashion-MNIST^[90]	70 000	784	10	图像	分类任务	[105],[45],[28],[58]
	Cifar10^[91]	60 000	3 072	10	图像	分类任务	[21],[27],[28],[105]
物体识别	Cifar100^[91]	60 000	3 072	100	图像	分类任务	[21]
	COCO-QA^[92]	78 736	—	—	图像、文本	分类任务	[51]
	NUS-WIDE^[93]	269 648	5 018	81	图像、文本	分类任务	[10],[27]
	CelebA^[94]	202 599	116 412	2	图像	分类任务	[58]
	News Popularity^[95]	93 239	11	—	时序文本	回归任务	[16],[24],[31],[33]
新闻媒体	Yahoo Answers^[96]	146 000	—	10	文本	分类任务	[21]
	Superconductivity^[97]	20 000	81	—	文本	回归任务	[29]
物理	Drive Diagnosis^[98]	58 509	48	11	文本	分类任务	[101]
家电能源	Appliances Energy^[99]	19 735	29	—	文本	回归任务	[20],[101]

框架或平台	开发者	加密手段	是否开源	使用类型	框架	特点	应用场景
FATE	微众银行	同态加密	是	研究、商用	Tensorflow、PyTorch	支持联邦迁移学习技术	车险定价、信贷风控等
PaddleFL	百度	安全多方计算	是	研究、商用	PaddlePaddle	基于全栈开源软件部署	计算机视觉、推荐算法等
Pysyft	OpenMined	同态加密、秘密共享	是	研究	TensorFlow、PyTorch	多种隐私控制策略	金融风控、广告推荐等
iBond	同盾科技	同态加密、秘密分享	否	商用	（未知）	引入知识联邦概念	自然语言处理、金融风控
PowerFL	腾讯	多方安全计算	否	商用	Angel	考虑数据异质性	金融风控、广告推荐

Survey on vertical federated learning: algorithm, privacy and security

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