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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 language | English |
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Title of host publication | 2019 IEEE 58th Conference on Decision and Control (CDC) |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 12 Mar 2020 |
Pages | 7203-7208 |
Article number | 9028969 |
ISBN (Print) | 978-1-7281-1399-9 |
ISBN (Electronic) | 978-1-7281-1398-2 |
DOIs | |
Publication status | Published - 12 Mar 2020 |
Event | 2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France Duration: 11 Dec 2019 → 13 Dec 2019 |
Conference
Conference | 2019 IEEE 58th Conference on Decision and Control (CDC) |
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Country/Territory | France |
City | Nice |
Period | 11/12/2019 → 13/12/2019 |
Series | I E E E Conference on Decision and Control. Proceedings |
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ISSN | 0743-1546 |
Keywords
- Privacy
- ADMM
- Encryption
- secret sharing
- Optimization
- Multiagent network
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Dive into the research topics of 'Privacy Preservation in Distributed Optimization via Dual Decomposition and ADMM'. Together they form a unique fingerprint.Projects
- 1 Finished
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SECURE: Secure Estimation and Control Using Recursion and Encryption
Wisniewski, R., Christensen, M. G., Andersen, A. O., Mannov, A., Geil, O. & Jessen, J. F.
01/04/2018 → 30/11/2021
Project: Research