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Cluster Computing

, Volume 21, Issue 1, pp 277–286 | Cite as

Privacy-preserving outsourced classification in cloud computing

  • Ping Li
  • Jin LiEmail author
  • Zhengan Huang
  • Chong-Zhi Gao
  • Wen-Bin Chen
  • Kai Chen
Article

Abstract

Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys, which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model

Keywords

Cryptography Privacy-preserving Machine learning Classification Homomorphic encryption 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61472091), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (2014A030306020), and Science and Technology Planning Project of Guangdong Province, China (2015B010129015)

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Ping Li
    • 1
  • Jin Li
    • 1
    Email author
  • Zhengan Huang
    • 1
  • Chong-Zhi Gao
    • 1
  • Wen-Bin Chen
    • 1
  • Kai Chen
    • 2
  1. 1.School of Computational Science & Education SoftwareGuangzhou UniversityGuangzhouPeople’s Republic of China
  2. 2.Institute of Information EngineeringChinese Academy of SciencesBeijingPeople’s Republic of China

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