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Differential Privacy of Aggregated DC Optimal Power Flow Data

Zhou, Fengyu and Anderson, James and Low, Steven H. (2019) Differential Privacy of Aggregated DC Optimal Power Flow Data. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190626-153106599

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Abstract

We consider the problem of privately releasing aggregated network statistics obtained from solving a DC optimal power flow (OPF) problem. It is shown that the mechanism that determines the noise distribution parameters are linked to the topology of the power system and the monotonicity of the network. We derive a measure of "almost" monotonicity and show how it can be used in conjunction with a linear program in order to release aggregated OPF data using the differential privacy framework.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1903.11237arXivDiscussion Paper
ORCID:
AuthorORCID
Anderson, James0000-0002-2832-8396
Low, Steven H.0000-0001-6476-3048
Additional Information:This work is funded by NSF grants CCF 1637598, ECCS 1619352, CNS 1545096, ARPA-E through grant de-ar0000699 and the GRID DATA program, and DTRA through grant HDTRA 1-15-1-0003.
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
NSFECCS-1619352
NSFCNS-1545096
Advanced Research Projects Agency-Energy (ARPA-E)DE-AR0000699
Defense Threat Reduction Agency (DTRA)HDTRA 1-15-1-0003
Record Number:CaltechAUTHORS:20190626-153106599
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190626-153106599
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96754
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 Jun 2019 01:51
Last Modified:27 Jun 2019 01:51

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