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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. In: 2019 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 1307-1314. ISBN 978-1-5386-7926-5.

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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:Book Section
Related URLs:
URLURL TypeDescription Paper
Zhou, Fengyu0000-0002-2639-6491
Anderson, James0000-0002-2832-8396
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2019 AACC. 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.
Funding AgencyGrant Number
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:
Official Citation:F. Zhou, J. Anderson and S. H. Low, "Differential Privacy of Aggregated DC Optimal Power Flow Data," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 1307-1314. URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96754
Deposited By: Tony Diaz
Deposited On:27 Jun 2019 01:51
Last Modified:02 Jun 2023 00:56

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