Achieving Privacy-Preserving and Verifiable Support Vector Machine Training in the Cloud

With the proliferation of machine learning, the cloud server has been employed to collect massive data and train machine learning models. Several privacy-preserving machine learning schemes have been suggested recently to guarantee data and model privacy in the cloud. However, these schemes either m...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security Vol. 18; p. 1
Main Authors: Hu, Chenfei, Zhang, Chuan, Lei, Dian, Wu, Tong, Liu, Ximeng, Zhu, Liehuang
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cloud computing
Computational modeling
Cryptography
Data models
data perturbation
homomorphic encryption
Machine learning
Perturbation
Privacy
Privacy-preserving
Servers
support vector machine
Support vector machines
Training
Verification
verification mechanism
ISSN:1556-6013, 1556-6021
Online Access:Get full text
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Summary:With the proliferation of machine learning, the cloud server has been employed to collect massive data and train machine learning models. Several privacy-preserving machine learning schemes have been suggested recently to guarantee data and model privacy in the cloud. However, these schemes either mandate the involvement of the data owner in model training or utilize high-cost cryptographic techniques, resulting in excessive computational and communication overheads. Furthermore, none of the existing work considers the malicious behavior of the cloud server during model training. In this paper, we propose the first privacy-preserving and verifiable support vector machine training scheme by employing a two-cloud platform. Specifically, based on the homomorphic verification tag, we design a verification mechanism to enable verifiable machine learning training. Meanwhile, to improve the efficiency of model training, we combine homomorphic encryption and data perturbation to design an efficient multiplication operation for the encryption domain. A rigorous theoretical analysis demonstrates the security and reliability of our scheme. The experimental results indicate that our scheme can reduce computational and communication overheads by at least 43.94% and 99.58%, respectively, compared to state-of-the-art SVM training methods.
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ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2023.3283104