基于目标扰动的AdaBoost算法

doi:10.11959/j.issn.1000-436x.2023028

Abstract

Abstract:

Aiming at the problem that the multi-round iteration process in the AdaBoost algorithm will amplify the noise added to achieve differential privacy protection, which leads to slow model convergence and greatly reduced data availability, an AdaBoost algorithm based on target perturbation—DPAda was proposed.Target perturbation was used to add noise to sample weights, accurately calculated their sensitivity, and a dynamic privacy budget was given.In order to solve the problem of excessive noise superposition, three noise injection algorithms based on swing sequence, random response and improved random response were proposed.The experimental results show that compared with DPAda_Random and DPAda_Swing, DPAda_Improved achieves the privacy protection of data, has higher classification accuracy, as well as better than other differential privacy AdaBoost algorithm, and can also solve the problem of excessive noise caused by continuous noise addition.

Key words: differential privacy, swing sequence, random response, privacy budget allocation, AdaBoost algorithm

CLC Number:

TP309.2

Shufen ZHANG, Yanling DONG, Jingcheng XU, Haoshi WANG. AdaBoost algorithm based on target perturbation[J]. Journal on Communications, 2023, 44(2): 198-209.

Figures/Tables 8

符号	含义
D_k	第k轮样本权值分布
w_{k ,i}	第k轮迭代中第i个样本权值
Δf	全局敏感度
G_k(x)	第k轮弱学习器
G (x)	最终分类器
${\tilde{w}}_{k, i}$	加噪后的w_k,i
${\bar{w}}_{k}$	第k轮样本权值均值
e_k	第k轮分类误差率
α_k	第k轮弱学习器权重
ε	隐私预算

References ［30］

［1］	陈天莹, 陈剑锋．大数据环境下的智能数据脱敏系统［J］．通信技术, 2016,49(7): 915-922．
	CHEN T Y , CHEN J F ． Intelligent data masking system for big data productive environment［J］． Communications Technology, 2016,49(7): 915-922．
［2］	SWEENEY L ． k-anonymity:a model for protecting privacy［J］． International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570．
［3］	GENTRY C , ． Fully homomorphic encryption using ideal lattices［C］// Proceedings of the 41st Annual ACM Symposium on Theory of Computing． New York:ACM Press, 2009: 169-178．
［4］	DWORK C , ． Differential privacy［C］// Proceedings of the 33rd International Conference on Automata,Languages and Programming (ICALP’06)． Berlin:Springer, 2006: 1-12．
［5］	DWORK C , AGRAWAL M , DU D ,et al． Differential Privacy:a survey of results［C］// International Conference on Theory and Applications of Models of Computation． Berlin:Springer, 2008: 1-19．
［6］	DWORK C , LEI J ． Differential privacy and robust statistics［C］// Proceedings of the 41st Annual ACM Symposium on Theory of Computing． New York:ACM Press, 2009: 371-380．
［7］	DWORK C , MCSHERRY F , NISSIM K ,et al． Calibrating noise to sensitivity in private data analysis［C］// Theory of Cryptography． Berlin:Springer, 2006: 265-284．
［8］	MCSHERRY F , TALWAR K ． Mechanism design via differential privacy［C］// Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science． Piscataway:IEEE Press, 2007: 94-103．
［9］	DWORK C , ROTH A ． The algorithmic foundations of differential privacy［J］． Foundations and Trends in Theoretical Computer Science, 2013,9(3/4): 211-407．
［10］	JAGANNATHAN G , PILLAIPAKKAMNATT K , WRIGHT R N ． A practical differentially private random decision tree classifier［C］// Proceedings of 2009 IEEE International Conference on Data Mining Workshops． Piscataway:IEEE Press, 2009: 114-121．
［11］	PATIL A , SINGH S ． Differential private random forest［C］// Proceedings of 2014 International Conference on Advances in Computing,Communications and Informatics (ICACCI)． Piscataway:IEEE Press, 2014: 2623-2630．
［12］	FLETCHER S , ISLAM M Z ． A differentially private decision forest［J］． Expert Systems with Applications, 2017,78: 16-31．
［13］	穆海蓉, 丁丽萍, 宋宇宁 ,等． DiffPRFs:一种面向随机森林的差分隐私保护算法［J］．通信学报, 2016,37(9): 175-182．
	MU H R , DING L P , SONG Y N ,et al． DiffPRFs:random forest under differential privacy［J］． Journal on Communications, 2016,37(9): 175-182．
［14］	李远航, 陈先来, 刘莉 ,等．面向差分隐私保护的随机森林算法［J］．计算机工程, 2020,46(1): 93-101．
	LI Y H , CHEN X L , LIU L ,et al． Random forest algorithm for differential privacy protection［J］． Computer Engineering, 2020,46(1): 93-101．
［15］	邓蔚, 陈秀婷, 张清华 ,等．基于树模型的差分隐私保护算法［J］．重庆邮电大学学报(自然科学版), 2020,32(5): 848-856．
	DENG W , CHEN X T , ZHANG Q H ,et al． Differential privacy protection algorithms based on tree model［J］． Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020,32(5): 848-856．
［16］	FLETCHER S , ISLAM M Z ． Differentially private random decision forests using smooth sensitivity［J］． Expert Systems With Applications, 2017,78: 16-31．
［17］	GUAN Z T , SUN X W , SHI L Y ,et al． A differentially private greedy decision forest classification algorithm with high utility［J］． Computers＆ Security, 2020,96:101930．
［18］	FLETCHER S , ISLAM M Z ． A differentially private random decision forest using reliable signal-to-noise ratios［C］// Australasian Joint Conference on Artificial Intelligence． Berlin:Springer, 2015: 192-203．
［19］	LI X X , LIU J , LIU S F ,et al． Differentially private ensemble learning for classification［J］． Neurocomputing, 2021,430: 34-46．
［20］	GAMBS S , KéGL B , A?MEUR E , ． Privacy-preserving boosting［J］． Data Mining and Knowledge Discovery, 2007,14(1): 131-170．
［21］	LI Y , BAI C X , REDDY C K ． A distributed ensemble approach for mining healthcare data under privacy constraints［J］． Information Sciences, 2016,330: 245-259．
［22］	沈思倩．关于差分隐私保护分类算法的研究［D］．南京:南京航空航天大学, 2017．
	SHEN S Q ． Research on the differential privacy classification algorithms［D］． Nanjing:Nanjing University of Aeronautics and Astronautics, 2017．
［23］	贾俊杰, 邱万勇, 马慧芳．差分隐私保护约束下集成分类算法的研究［J］．信息安全学报, 2021,6(4): 106-118．
	JIA J J , QIU W Y , MA H F ． Research on an ensemble classification algorithm under differential privacy［J］． Journal of Cyber Security, 2021,6(4): 106-118．
［24］	LI Q B , WU Z M , WEN Z Y ,et al． Privacy-preserving gradient boosting decision trees［J］． Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(1): 784-791．
［25］	FREUND Y , SCHAPIRE R E ． A decision-theoretic generalization of on-line learning and an application to boosting［J］． Journal of Computer and System Sciences, 1997,55(1): 119-139．
［26］	FREUND Y , SCHAPIRE R E ． Experiments with a new Boosting algorithm［C］// Proceedings of the 13th Conference on Machine Learning．San Francisco:Morgan Kaufmann Publishers Inc． , 1996: 148-156．
［27］	曹莹, 苗启广, 刘家辰 ,等． AdaBoost 算法研究进展与展望［J］．自动化学报, 2013,39(6): 745-758．
	CAO Y , MIAO Q G , LIU J C ,et al． Advance and prospects of AdaBoost algorithm［J］． Acta Automatica Sinica, 2013,39(6): 745-758．
［28］	FRIEDMAN J , HASTIE T , TIBSHIRANI R ． Additive logistic regression:a statistical view of boosting［J］． The Annals of Statistics, 2000,28(2): 337-407．
［29］	燕会霞．简谐振动与摆动数列的通项公式［J］．数学教学, 2006(9): 19-20．
	YAN H X ． General formula of simple harmonic vibration and swing sequence［J］． Mathematics teaching, 2006(9): 19-20．
［30］	邱万勇．基于差分隐私的集成分类算法及应用研究［D］．兰州:西北师范大学, 2021．
	QIU W Y ． A study of ensemble classification algorithms and applications based on differential privacy［D］． Lanzhou:Northwest Normal University, 2021．

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数据集名称	样本数	连续型特征数	离散型特征数	类数
Adult_train	325 61	6	8	2
Adult_test	162 81	6	8	2
Bank Marketing	411 88	10	10	2

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AdaBoost algorithm based on target perturbation

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