Uplink communication efficient differentially private sparse optimization with feature-wise distributed data

Jian Lou, Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Preserving differential privacy during empirical risk minimization model training has been extensively studied under centralized and sample-wise distributed dataset settings. This paper considers a nearly unexplored context with features partitioned among different parties under privacy restriction. Motivated by the nearly optimal utility guarantee achieved by centralized private Frank-Wolfe algorithm (Talwar, Thakurta, and Zhang 2015), we develop a distributed variant with guaranteed privacy, utility and uplink communication complexity. To obtain these guarantees, we provide a much generalized convergence analysis for block-coordinate Frank-Wolfe under arbitrary sampling, which greatly extends known convergence results that are only applicable to two specific block sampling distributions. We also design an active feature sharing scheme by utilizing private Johnson-Lindenstrauss transform, which is the key to updating local partial gradients in a differentially private and communication efficient manner.

Original languageEnglish
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages125-133
Number of pages9
ISBN (Electronic)9781577358008
Publication statusPublished - 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Conference

Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/TerritoryUnited States
CityNew Orleans
Period2/02/187/02/18

Scopus Subject Areas

  • Artificial Intelligence

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