Local differential privacy for multi-agent distributed optimal power flow

Roel Dobbe*, Ye Pu, Jingge Zhu, Kannan Ramchandran, Claire Tomlin

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Real-time data-driven optimization and control problems over networks, such as in traffic or energy systems, may require sensitive information of participating agents to calculate solutions and decision variables. Adversaries with access to coordination signals may potentially decode information on individual agents and put privacy at risk. We use the Inexact Alternating Minimization Algorithm to instantiate local differential privacy for distributed optimization, addressing situations in which individual agents need to protect their individual data, in the form of optimization parameters, from all other agents and any central authority. This mechanism allows agents to customize their own privacy level based on local needs and parameter sensitivities. The resulting algorithm works across a large family of convex distributed optimization problems. We implement the method on a distributed optimal power flow problem that aims to prevent overload on critical branches in a radial network.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Number of pages5
ISBN (Electronic)9781728171005
Publication statusPublished - 2020
Event10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020 - Virtual/online event due to COVID-19, Delft, Netherlands
Duration: 26 Oct 202028 Oct 2020
Conference number: 10

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe


Conference10th IEEE PES Innovative Smart Grid Technologies Europe, ISGT-Europe 2020
Abbreviated titleISGT-Europe 2020
OtherVirtual/online event due to COVID-19
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