# Realistic Differentially-Private Transmission Power Flow Data Release

arXiv: https://arxiv.org/abs/2103.14036 · HTML: https://mlsanity.com/paper/2103.14036/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms](https://mlsanity.com/paper/1908.02788/) (2019)
- [PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations](https://mlsanity.com/paper/1711.01728/) (2017)
- [Differentially Private Distributed Constrained Optimization](https://mlsanity.com/paper/1411.4105/) (2014)
- [NESTA, The NICTA Energy System Test Case Archive](https://mlsanity.com/paper/1411.0359/) (2014)
- [Differential Privacy for Power Grid Obfuscation](https://mlsanity.com/paper/1901.06949/) (2019)
- [Differential Privacy of Aggregated DC Optimal Power Flow Data](https://mlsanity.com/paper/1903.11237/) (2019)
- [A Privacy-Preserving Distributed Control of Optimal Power Flow](https://mlsanity.com/paper/2102.02276/) (2021)
- [Differentially Private Optimal Power Flow for Distribution Grids](https://mlsanity.com/paper/2004.03921/) (2020)
- [Recent Advances in Computational Methods for the Power Flow Equations](https://mlsanity.com/paper/1510.00073/) (2015)
- [Linear Approximations to AC Power Flow in Rectangular Coordinates](https://mlsanity.com/paper/1512.06903/) (2015)
- [Privacy-Preserving Obfuscation for Distributed Power Systems](https://mlsanity.com/paper/1910.04250/) (2019)
- [Differentially Private Convex Optimization with Feasibility Guarantees](https://mlsanity.com/paper/2006.12338/) (2020)
- [Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids](https://mlsanity.com/paper/1703.02382/) (2017)

## Abstract (arXiv)

For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers. This critical challenge has recently been somewhat addressed in [1]. This paper significantly extends this existing work. First, we reduce the potential leakage information by proposing a fundamentally different post-processing method, using public information of grid losses rather than power dispatch, which achieve a higher level of privacy protection. Second, we protect more sensitive parameters, i.e., branch shunt susceptance in addition to series impedance (complete pi-model). This protects power flow data for the transmission high-voltage networks, using differentially private transformations that maintain the optimal power flow consistent with, and faithful to, expected model behaviour. Third, we tested our approach at a larger scale than previous work, using the PGLib-OPF test cases [10]. This resulted in the successful obfuscation of up to a 4700-bus system, which can be successfully solved with faithfulness of parameters and good utility to data analysts. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees, while maintaining solvability, fidelity and feasibility of the system.