Privacy-Preserving Obfuscation for Distributed Power Systems

Abstract

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.

arXiv commentTotal 10 pages: main body 8 pages, reference 1 page, appendix 2 pages

References in corpus (5)

• Geo-Indistinguishability: Differential Privacy for Location-Based Systems
• NESTA, The NICTA Energy System Test Case Archive
• Privacy-Preserving Obfuscation for Distributed Power Systems
• Optimality of the Laplace Mechanism in Differential Privacy
• Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids

Cited by in corpus (6)

• Privacy-Preserving Obfuscation for Distributed Power Systems
• Privacy-Preserved Aggregate Thermal Dynamic Model of Buildings
• Differentially Private Convex Optimization with Feasibility Guarantees
• Privacy-Preserving Distributed Optimal Power Flow with Partially Homomorphic Encryption
• Decentralized and Secure Generation Maintenance with Differential Privacy
• Bilevel Optimization for Differentially Private Optimization in Energy Systems