# Privacy-Preserving Obfuscation for Distributed Power Systems

arXiv: https://arxiv.org/abs/1910.04250 · HTML: https://mlsanity.com/paper/1910.04250/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF) problems and proposes a distributed algorithm that complies with the notion of Differential Privacy, a strong privacy framework used to bound the risk of re-identification. The problem is challenging since the application of traditional differential privacy mechanisms to the load data fundamentally changes the nature of the underlying optimization problem and often leads to severe feasibility issues. The proposed differentially private distributed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) and guarantees that the released privacy-preserving data retains high fidelity and satisfies the AC power flow constraints. Experimental results on a variety of OPF benchmarks demonstrate the effectiveness of the approach.