# Differential Privacy of Aggregated DC Optimal Power Flow Data

arXiv: https://arxiv.org/abs/1903.11237 · HTML: https://mlsanity.com/paper/1903.11237/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Collaborative Bayesian Optimization via Wasserstein Barycenters](https://mlsanity.com/paper/2504.10770/) (2025)
- [Differentially Private Algorithms for Synthetic Power System Datasets](https://mlsanity.com/paper/2303.11079/) (2023)
- [Realistic Differentially-Private Transmission Power Flow Data Release](https://mlsanity.com/paper/2103.14036/) (2021)
- [HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation](https://mlsanity.com/paper/2111.07015/) (2021)

## Abstract (arXiv)

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.