Differential Privacy of Aggregated DC Optimal Power Flow Data
This addresses privacy concerns for aggregated power grid data, but it is incremental as it builds on existing differential privacy methods for a specific domain.
The paper tackles the problem of privately releasing aggregated network statistics from DC optimal power flow (OPF) problems by linking noise distribution parameters to power system topology and monotonicity, deriving an 'almost' monotonicity measure and using a linear program to achieve differential privacy.
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.