Privacy-Preserving Obfuscation for Distributed Power Systems

Autor:	Mak, Terrence W. K., Fioretto, Ferdinando, Van Hentenryck, Pascal
Rok vydání:	2019
Předmět:	Mathematics - Optimization and Control Computer Science - Cryptography and Security Computer Science - Multiagent Systems Electrical Engineering and Systems Science - Systems and Control
Zdroj:	IEEE Transactions on Power Systems ( Volume: 35 , Issue: 2 , March 2020 )
Druh dokumentu:	Working Paper
DOI:	10.1109/TPWRS.2019.2945069
Popis:	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. Comment: Total 10 pages: main body 8 pages, reference 1 page, appendix 2 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.04250 Zobrazit plný text záznamu View this record from Arxiv