Differential Privacy of Aggregated DC Optimal Power Flow Data
Cryptography and Security
2019-03-28 v1 Optimization and Control
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.
Keywords
Cite
@article{arxiv.1903.11237,
title = {Differential Privacy of Aggregated DC Optimal Power Flow Data},
author = {Fengyu Zhou and James Anderson and Steven H. Low},
journal= {arXiv preprint arXiv:1903.11237},
year = {2019}
}
Comments
Accepted by 2019 American Control Conference (ACC)