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Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data

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Machine Learning for Cyber Security (ML4CS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11806))

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Abstract

Decision trees are famous machine learning classifiers which have been widely used in many areas, such as healthcare, text classification and remote diagnostics, etc. The service providers usually host a decision tree model on the cloud server and provide some classification service for clients to use such a model remotely. In such a scenario, the model is a valuable asset to the cloud which should not be disclosed to the clients, while the query data and classification results are private to the client. To solve such a problem, we propose several building blocks, i.e., secure comparison and secure polynomial calculation, in a two-cloud model. Based on these building blocks, we design a privacy-preserving decision tree evaluation scheme. Compared with the most recent works, our scheme can fully protect the tree model and clients’ data privacy simultaneously. Besides, our scheme also supports offline service users which is essential to the system’s scalability. Moreover, through theoretical analysis and real-world experimental test, it is oblivious that our scheme is quite efficient.

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Notes

  1. 1.

    UC Irvine Machine Learning Repository https://archive.ics.uci.edu/ml/datasets.htm.

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Acknowledgement

The work is supported by the National Key Research and Development Program under grant 2017YFB0802300, the National Natural Science Foundation of China (No. 61702541, No. 61702105), the Young Elite Scientists Sponsorship Program by CAST (2017QNRC001), the Science and Technology Research Plan Program by NUDT (Grant No. ZK17-03-46), and Guangxi Cloud Computing and Large Data Collaborative Innovation Center Project.

Author information

Authors and Affiliations

  1. School of Computer, National University of Defense Technology, Changsha, China

    Lin Liu, Jinshu Su, Rongmao Chen, Jinrong Chen & Jie Li

  2. National Key Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China

    Jinshu Su

  3. National University of Defense Technology, Changsha, China

    Guangliang Sun

Authors
  1. Lin Liu
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  2. Jinshu Su
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  3. Rongmao Chen
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  4. Jinrong Chen
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  5. Guangliang Sun
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  6. Jie Li
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Corresponding authors

Correspondence to Jinshu Su or Rongmao Chen .

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Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

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Liu, L., Su, J., Chen, R., Chen, J., Sun, G., Li, J. (2019). Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_26

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  • DOI: https://doi.org/10.1007/978-3-030-30619-9_26

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  • Online ISBN: 978-3-030-30619-9

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