Differential privacy preservation for deep auto-encoders: An application of human behavior prediction

Nhat Hai Phan, Yue Wang, Xintao Wu, Dejing Dou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

93 Scopus citations

Abstract

In recent years, deep learning has spread beyond both academia and industry with many exciting real-world applications. The development of deep learning has presented obvious privacy issues. However, there has been lack of scientific study about privacy preservation in deep learning. In this paper, we concentrate on the auto-encoder, a fundamental component in deep learning, and propose the deep private auto-encoder (dPA). Our main idea is to enforce -differential privacy by perturbing the objective functions of the traditional deep auto-encoder, rather than its results.We apply the dPA to human behavior prediction in a health social network. Theoretical analysis and thorough experimental evaluations show that the dPA is highly effective and efficient, and it significantly outperforms existing solutions.

Original languageEnglish (US)
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Pages1309-1316
Number of pages8
ISBN (Electronic)9781577357605
StatePublished - 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Other

Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period2/12/162/17/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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