AdaBoost algorithm based on target perturbation

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 pr...

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Bibliographic Details
Published inTongxin Xuebao Vol. 44; pp. 198 - 209
Main Authors Shufen ZHANG, Yanling DONG, Jingcheng XU, Haoshi WANG
Format Journal Article
LanguageChinese
Published Editorial Department of Journal on Communications 01.02.2023
Subjects
AdaBoost algorithm
differential privacy
privacy budget allocation
random response
swing sequence
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ISSN1000-436X

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Summary: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.
ISSN:1000-436X