Privacy Preservation in Distributed Optimization via Dual Decomposition and ADMM

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Abstract

In this work, we explore distributed optimization problems, as they are often stated in energy and resource optimization. More precisely, we consider systems consisting of a number of subsystems that are solely connected through linear constraints on the optimized solutions. The focus is put on two approaches; namely dual decomposition and alternating direction method of multipliers (ADMM), and we are interested in the case where it is desired to keep information about subsystems secret. To this end, we propose a privacy preserving algorithm based on secure multiparty computation (SMPC) and secret sharing that ensures privacy of the subsystems while converging to the optimal solution. To gain efficiency in our method, we modify the traditional ADMM algorithm.

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control (CDC)
Number of pages6
PublisherIEEE
Publication date12 Mar 2020
Pages7203-7208
Article number9028969
ISBN (Print)978-1-7281-1399-9
ISBN (Electronic)978-1-7281-1398-2
DOIs
Publication statusPublished - 12 Mar 2020
Event2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France
Duration: 11 Dec 201913 Dec 2019

Conference

Conference2019 IEEE 58th Conference on Decision and Control (CDC)
CountryFrance
CityNice
Period11/12/201913/12/2019
SeriesI E E E Conference on Decision and Control. Proceedings
ISSN0743-1546

Keywords

  • Privacy
  • ADMM
  • Encryption
  • secret sharing
  • Optimization
  • Multiagent network

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