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A Privacy-Preserving Classifier in Statistic Pattern Recognition

  • Qi Wang
  • Dehua ZhouEmail author
  • Quanlong Guan
  • Yanling Li
  • Jimian Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11064)

Abstract

Machine learning classification and pattern recognition are widely used in various scenarios nowadays, such as medical diagnosis and face recognition, both need to estimate the similarity measure between different samples. In such applications, it is critical to protect the privacy of both the private input data and the machine learning model. In this paper, we propose a privacy-preserving Mahalanobis distance scheme for the statistic pattern recognition, and construct a protocol for the privacy-preserving prediction phase by using the labeled-homomorphic encryption scheme, which combines linearly homomorphic encryption and pseudo-random function. And we consider an outsouring scenario, which most work to be outsourced to the cloud server. Our design goal is to ensure that the client’s private data is permanently confidential and protect the secret model in cloud server. Most of the previous work proposed complex schemes with too many interactions between the clients and cloud server, and we propose an efficient scheme to minimal the complexity of the client side.

Keywords

Classification Homomorphic encryption Cloud computing 

Notes

Acknowledgements

This work is supported by NSFC (61602210), Science and Technology Project of Guangzhou City (No. 201707010320), Natural Science Foundation of Guangdong Province (No. 2014A030310156), the Fundamental Research Funds for the Central Universities (21617408), the Science and Technology Planning Project of Guangdong Province, China (2014A040401027, 2015A030401043, 2017A040405029).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qi Wang
    • 1
  • Dehua Zhou
    • 1
    Email author
  • Quanlong Guan
    • 2
  • Yanling Li
    • 1
  • Jimian Yang
    • 1
  1. 1.Department of Computer ScienceJinan UniversityGuangzhouChina
  2. 2.Network and Educational Technology CenterJinan UniversityGuangzhouChina

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