Smart Meter Privacy

The new generation electricity supply network, called the smart grid (SG), provides consumers with an active management and control of the power. By utilizing digital communications and sensing technologies which make the grid smart, SGs yield more efficient electricity transmission, reduced peak demand, improved security and increased integration of renewable energy systems compared to the traditional grid. Smart meters (SMs) are one of the core enablers of SG systems; they measure and record the high resolution electricity consumption information of a household almost in a real time basis, and report it to the utility provider (UP) at regular time intervals. SM measurements can be used for time-of-use pricing, trading user-generated energy, and mitigating load variations. However, real-time SM readings can also reveal sensitive information about the consumer’s activities which the user may not want to share with the UP, resulting in serious privacy concerns. SM privacy enabling techniques proposed in the literature can be categorized as SM data manipulation and demand shaping. While the SM data is modified before being reported to the UP in the former method, the latter requires direct manipulation of the real energy consumption by exploiting physical resources, such as a renewable energy source (RES) or a rechargeable battery (RB). In this chapter, a data manipulation privacy-enabling technique and three different demand shaping privacy-enabling techniques are presented, considering SM with a RES and an RB, SM with only an RB and SM with only a RES. Information theoretic measures are used to quantify SM privacy. Optimal energy management strategies and bounds which are obtained using control theory, specifically Markov decision processes (MDPs), and rate distortion theory are analyzed.

[1]  Tobias J. Oechtering,et al.  Privacy Against a Hypothesis Testing Adversary , 2018, IEEE Transactions on Information Forensics and Security.

[2]  Santiago Grijalva,et al.  Modeling for Residential Electricity Optimization in Dynamic Pricing Environments , 2012, IEEE Transactions on Smart Grid.

[3]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[4]  R Bellman,et al.  On the Theory of Dynamic Programming. , 1952, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Stephen Makonin,et al.  Approaches to Non-Intrusive Load Monitoring (NILM) in the Home , 2012 .

[6]  Bart Jacobs,et al.  Privacy-Friendly Energy-Metering via Homomorphic Encryption , 2010, STM.

[7]  H. Vincent Poor,et al.  Privacy-Aware Smart Metering: Progress and Challenges , 2018, IEEE Signal Processing Magazine.

[8]  Josep Domingo-Ferrer,et al.  From t-Closeness-Like Privacy to Postrandomization via Information Theory , 2010, IEEE Transactions on Knowledge and Data Engineering.

[9]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

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

[11]  Shigenobu Kobayashi,et al.  Privacy-preserving reinforcement learning , 2008, ICML '08.

[12]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[13]  Deniz Gündüz,et al.  On Perfect Privacy , 2017, 2018 IEEE International Symposium on Information Theory (ISIT).

[14]  H. Vincent Poor,et al.  Increasing Smart Meter Privacy Through Energy Harvesting and Storage Devices , 2013, IEEE Journal on Selected Areas in Communications.

[15]  Tamás Linder,et al.  Approximations for Partially Observed Markov Decision Processes , 2018 .

[16]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[17]  Georgios Kalogridis,et al.  Smart Grid Privacy via Anonymization of Smart Metering Data , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[18]  Onur Tan,et al.  Privacy-Cost Trade-offs in Demand-Side Management With Storage , 2017, IEEE Transactions on Information Forensics and Security.

[19]  Bert-Jaap Koops,et al.  Smart Metering and Privacy in Europe: Lessons from the Dutch Case , 2013, European Data Protection.

[20]  Prashant J. Shenoy,et al.  SmartCharge: Cutting the electricity bill in smart homes with energy storage , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[21]  Ashish Khisti,et al.  Information-Theoretic Privacy for Smart Metering Systems with a Rechargeable Battery , 2015, IEEE Transactions on Information Theory.

[22]  Inaki Esnaola,et al.  Smart meter privacy via the trapdoor channel , 2017, 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[23]  Mani B. Srivastava,et al.  Cooperative state estimation for preserving privacy of user behaviors in smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[24]  Tobias J. Oechtering,et al.  Privacy-preserving smart meter control strategy including energy storage losses , 2018, 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[25]  Parv Venkitasubramaniam,et al.  On the privacy-cost tradeoff of an in-home power storage mechanism , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[26]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[27]  H. Vincent Poor,et al.  Smart Meter Privacy With Renewable Energy and an Energy Storage Device , 2017, IEEE Transactions on Information Forensics and Security.

[28]  Deniz Gündüz,et al.  Smart meter privacy with renewable energy and a finite capacity battery , 2016, 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[29]  Ufuk Topcu,et al.  Event-based information-theoretic privacy: A case study of smart meters , 2016, 2016 American Control Conference (ACC).

[30]  Deniz Gündüz,et al.  Smart Meter Privacy for Multiple Users in the Presence of an Alternative Energy Source , 2013, IEEE Transactions on Information Forensics and Security.

[31]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[32]  Liang Cheng,et al.  Evaluation of utility-privacy trade-offs of data manipulation techniques for smart metering , 2016, 2016 IEEE Conference on Communications and Network Security (CNS).

[33]  H. Vincent Poor,et al.  Smart meter privacy: A utility-privacy framework , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[34]  Deniz Gündüz,et al.  Privacy-cost Trade-off in a Smart Meter System with a Renewable Energy Source and a Rechargeable Battery , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  Gabriela Hug,et al.  Privacy-Protecting Energy Management Unit Through Model-Distribution Predictive Control , 2016, IEEE Transactions on Smart Grid.