Differential Privacy for Power Grid Obfuscation

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to privacy and security as they can reveal sensitive commercial information and expose system vulnerabilities. This paper investigates how to release the data for power networks where the parameters of transmission lines and transformers are obfuscated. It does so by using the framework of Differential Privacy (DP), that provides strong privacy guarantees and has attracted significant attention in recent years. Unfortunately, simple DP mechanisms often result in AC-infeasible networks. To address these concerns, this paper presents a novel differentially private mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated power network. Experimental results also show that the obfuscation significantly reduces the potential damage of an attack carried by exploiting the released dataset.

[1]  Russell Bent,et al.  PowerModels.J1: An Open-Source Framework for Exploring Power Flow Formulations , 2017, 2018 Power Systems Computation Conference (PSCC).

[2]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[3]  Steven H. Low,et al.  Differential Privacy of Aggregated DC Optimal Power Flow Data , 2019, 2019 American Control Conference (ACC).

[4]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2016, J. Priv. Confidentiality.

[5]  George J. Pappas,et al.  Optimality of the Laplace Mechanism in Differential Privacy , 2015, ArXiv.

[6]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[7]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.

[8]  EiblGünther,et al.  Differential privacy for real smart metering data , 2017 .

[9]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[10]  Pascal Van Hentenryck,et al.  Convex quadratic relaxations for mixed-integer nonlinear programs in power systems , 2016, Mathematical Programming Computation.

[11]  R. Jabr Radial distribution load flow using conic programming , 2006, IEEE Transactions on Power Systems.

[12]  J. Douglas,et al.  Electric utility responses to grid security issues , 2006, IEEE Power and Energy Magazine.

[13]  Claude Castelluccia,et al.  I Have a DREAM! (DiffeRentially privatE smArt Metering) , 2011, Information Hiding.

[14]  Pascal Van Hentenryck,et al.  Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately - Releasing Optimal Power Flow Benchmarks Privately , 2018, CPAIOR.

[15]  Dominik Engel,et al.  Differential privacy for real smart metering data , 2017, Computer Science - Research and Development.

[16]  Abhishek Halder,et al.  Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity , 2017, IEEE Transactions on Power Systems.

[17]  Jing Zhao,et al.  Achieving differential privacy of data disclosure in the smart grid , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[18]  Zeyar Aung,et al.  Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids , 2017, 2017 IEEE Trustcom/BigDataSE/ICESS.

[19]  Catuscia Palamidessi,et al.  Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.

[20]  Pascal Van Hentenryck,et al.  Privacy-Preserving Obfuscation of Critical Infrastructure Networks , 2019, IJCAI.

[21]  Hartmut Schmeck,et al.  The influence of differential privacy on short term electric load forecasting , 2018, ArXiv.

[22]  Salil P. Vadhan,et al.  The Complexity of Differential Privacy , 2017, Tutorials on the Foundations of Cryptography.

[23]  Carleton Coffrin,et al.  The QC Relaxation: A Theoretical and Computational Study on Optimal Power Flow , 2017, IEEE Transactions on Power Systems.

[24]  Carleton Coffrin,et al.  NESTA, The NICTA Energy System Test Case Archive , 2014, ArXiv.

[25]  Raheem A. Beyah,et al.  Di-PriDA: Differentially Private Distributed Load Balancing Control for the Smart Grid , 2019, IEEE Transactions on Dependable and Secure Computing.