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Classification Learning from Private Data in Heterogeneous Settings

  • Yiwen NieEmail author
  • Shaowei Wang
  • Wei Yang
  • Liusheng Huang
  • Zhenhua Zhao
Conference paper
  • 2.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Classification is useful for mining labels of data. Though well-trained classifiers benefit many applications, their training procedures on user-contributed data may leak users’ privacy.

This work studies methods for private model training in heterogeneous settings, specially for the Naïve Bayes Classifier (NBC). Unlike previous works focusing on centralized and consistent datasets, we consider the private training in two more practical settings, namely the local setting and the mixture setting. In the local setting, individuals directly contribute training tuples to the untrusted trainer. In the mixture setting, the training dataset is composed of individual tuples and statistics of datasets from institutes. We propose a randomized response based NBC strategy for the local setting. To cope with the privacy of heterogeneous data (single tuples and the statistics) in the mixture setting, we design a unified privatized scheme. It integrates respective sanitization strategies on the two data types while preserving privacy. Besides contributing error bounds of estimated probabilities constituting NBC, we prove their optimality in the minimax framework and quantify the classification error of the privately learned NBC. Our analyses are validated with extensive experiments on real-world datasets.

Keywords

Differential privacy Classification 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61572456), the Anhui Province Guidance Funds for Quantum Communication and Quantum Computers and the Natural Science Foundation of Jiangsu Province of China (No. BK20151241).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yiwen Nie
    • 1
    Email author
  • Shaowei Wang
    • 1
  • Wei Yang
    • 1
  • Liusheng Huang
    • 1
  • Zhenhua Zhao
    • 1
  1. 1.University of Science and Technology of ChinaHefeiChina

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