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Privacy-preserving outsourced classification in cloud computing

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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

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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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Authors and Affiliations

  1. School of Computational Science & Education Software, Guangzhou University, Guangzhou, 510006, People’s Republic of China

    Ping Li, Jin Li, Zhengan Huang, Chong-Zhi Gao & Wen-Bin Chen

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Kai Chen

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  1. Ping Li
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  2. Jin Li
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  3. Zhengan Huang
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  4. Chong-Zhi Gao
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Correspondence to Jin Li.

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Li, P., Li, J., Huang, Z. et al. Privacy-preserving outsourced classification in cloud computing. Cluster Comput 21, 277–286 (2018). https://doi.org/10.1007/s10586-017-0849-9

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