A dual perturbation approach for differential private admm-based distributed empirical risk minimization

Tao Zhang, Quanyan Zhu

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

Abstract

The rapid growth of data has raised the importance of privacy-preserving techniques in distributed machine learning. In this paper, we develop a privacy-preserving method to a class of regularized empirical risk minimization (ERM) machine learning problems. We first decentralize the learning algorithm using the alternating direction method of multipliers (ADMM), and propose the method of dual variable perturbation to provide dynamic differential privacy. The mechanism leads to a privacy-preserving algorithm under mild conditions of the convexity and differentiability of the loss function and the regularizer. We study the performance of the algorithm measured by the number of data points required to achieve a bounded error. To design an optimal privacy mechanism, we analyze the fundamental tradeoff between privacy and accuracy, and provide guidelines to choose privacy parameters. Numerical experiments using the realworld database are performed to corroborate the results on the privacy and utility tradeoffs and design.

Original languageEnglish (US)
Title of host publicationAISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016
PublisherAssociation for Computing Machinery, Inc
Pages129-137
Number of pages9
ISBN (Electronic)9781450345736
DOIs
StatePublished - Oct 28 2016
Event9th ACM Workshop on Artificial Intelligence and Security, AISec 2016 - Vienna, Austria
Duration: Oct 28 2016 → …

Publication series

NameAISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016

Other

Other9th ACM Workshop on Artificial Intelligence and Security, AISec 2016
CountryAustria
CityVienna
Period10/28/16 → …

Keywords

  • ADMM
  • Differential privacy
  • Distributed optimization
  • Machine learning
  • Privacy tradeoffs

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Zhang, T., & Zhu, Q. (2016). A dual perturbation approach for differential private admm-based distributed empirical risk minimization. In AISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016 (pp. 129-137). (AISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/2996758.2996762