首页|IHVFL:a privacy-enhanced intention-hiding vertical federated learning framework for medical data
IHVFL:a privacy-enhanced intention-hiding vertical federated learning framework for medical data
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Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,while the preparation of training data receives little attention.In real-world applications,like smart healthcare,the process of the training data preparation may involve some participant's intention which could be privacy information for this partici-pant.To protect the privacy of the model training intention,we describe the idea of Intention-Hiding Vertical Feder-ated Learning(IHVFL)and illustrate a framework to achieve this privacy-preserving goal.First,we construct two secure screening protocols to enhance the privacy protection in feature engineering.Second,we implement the work of sample alignment bases on a novel private set intersection protocol.Finally,we use the logistic regression algorithm to demonstrate the process of IHVFL.Experiments show that our model can perform better efficiency(less than 5min)and accuracy(97％)on Breast Cancer medical dataset while maintaining the intention-hiding goal.
Medical dataVertical federated learningPrivacy-presservingIntention-hidingLogistic regression
Fei Tang、Shikai Liang、Guowei Ling、Jinyong Shan
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College of Computer Science and Technology,Chongqing University of Posts and Telecommunications,No.2,Chongwen Road,Nan'an District,Chongqing 400065,China
School of Cyber Security and Information Law,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
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