Differential Privacy of Aggregated DC Optimal Power Flow Data

@article{Zhou2019DifferentialPO,
  title={Differential Privacy of Aggregated DC Optimal Power Flow Data},
  author={Fengyu Zhou and James Anderson and Steven H. Low},
  journal={2019 American Control Conference (ACC)},
  year={2019},
  pages={1307-1314}
}
  • Fengyu Zhou, James Anderson, Steven H. Low
  • Published in
    American Control Conference…
    2019
  • Computer Science, Mathematics
  • 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. 

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