Consumer privacy protection using flexible thermal loads: Theoretical limits and practical considerations

Abstract The increasing adoption of smart meters introduces growing concerns about consumer privacy risks stemming from high resolution metering data. To counter these risks, there have been various works in actively shaping the grid-visible energy consumption profile using controllable loads such as energy storage systems (ESSs) and flexible consumer loads. In this paper, we compare the use of flexible thermal-based consumer loads (FTLs) against ESSs for consumer privacy protection. By first assuming ideal conditions, and subsequently bringing them closer to reality, the limitations of using FTLs for privacy protection are identified. Through theoretical analyses and realistic simulations, it is shown that, due to the limitations in the operation of FTLs, without significant over-sizing of systems and sacrifices in consumer comfort, FTLs of much higher equivalent energy storage capacity are required to afford the same level of protection as ESSs. Nonetheless, given their increasing ubiquity, controllable FTLs should be considered for use in consumer privacy protection.

[1]  Binbin Chen,et al.  Towards quantitative evaluation of privacy protection schemes for electricity usage data sharing , 2018, ICT Express.

[2]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[3]  Dane Christensen,et al.  Foresee: A user-centric home energy management system for energy efficiency and demand response , 2017 .

[4]  Prashant J. Shenoy,et al.  Preventing Occupancy Detection From Smart Meters , 2015, IEEE Transactions on Smart Grid.

[5]  Ian Richardson,et al.  Smart meter data: Balancing consumer privacy concerns with legitimate applications , 2012 .

[6]  Yuan Qi,et al.  Minimizing private data disclosures in the smart grid , 2012, CCS '12.

[7]  Klemens Böhm,et al.  Privacy Measures and Storage Technologies for Battery-Based Load Hiding - an Overview and Experimental Study , 2020, e-Energy.

[8]  George Danezis,et al.  Smart meter aggregation via secret-sharing , 2013, SEGS '13.

[9]  Xiaodong Lin,et al.  Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering , 2017, IEEE Transactions on Smart Grid.

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

[11]  Giacomo Verticale,et al.  A data pseudonymization protocol for Smart Grids , 2012, 2012 IEEE Online Conference on Green Communications (GreenCom).

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

[13]  Prashant J. Shenoy,et al.  Private memoirs of a smart meter , 2010, BuildSys '10.

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

[15]  Vincent W. S. Wong,et al.  Smart Meter Privacy: Exploiting the Potential of Household Energy Storage Units , 2018, IEEE Internet of Things Journal.

[16]  Jinjun Chen,et al.  Differential privacy for renewable energy resources based smart metering , 2019, J. Parallel Distributed Comput..

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

[18]  Xin Jin,et al.  Model Predictive Control of Heat Pump Water Heaters for Energy Efficiency , 2014 .

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

[20]  Gabriela Hug,et al.  Considering Time Correlation in the Estimation of Privacy Loss for Consumers with Smart Meters , 2018, 2018 Power Systems Computation Conference (PSCC).

[21]  Dmitry Podkuiko,et al.  Multi-vendor penetration testing in the advanced metering infrastructure , 2010, ACSAC '10.

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

[23]  Youngjoo Shin,et al.  Privacy-Preserving Aggregation and Authentication of Multi-Source Smart Meters in a Smart Grid System , 2017 .

[24]  Yonghong Kuang,et al.  Smart home energy management systems: Concept, configurations, and scheduling strategies , 2016 .

[25]  Aidan Duffy,et al.  The Generation of Domestic Electricity Load Profiles through Markov Chain Modelling , 2010 .

[26]  H. Vincent Poor,et al.  Smart Meter Privacy: A Theoretical Framework , 2013, IEEE Transactions on Smart Grid.

[27]  Erik Poll,et al.  Smart metering in the Netherlands: What, how, and why , 2019, International Journal of Electrical Power & Energy Systems.

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

[29]  Jenifer Sunrise Winter,et al.  Citizen Perspectives on the Customization/Privacy Paradox Related to Smart Meter Implementation , 2015, Int. J. Technoethics.

[30]  Jian Weng,et al.  Cost-Friendly Differential Privacy for Smart Meters: Exploiting the Dual Roles of the Noise , 2017, IEEE Transactions on Smart Grid.

[31]  Abdelrahaman Aly,et al.  A Secure and Privacy-Preserving Protocol for Smart Metering Operational Data Collection , 2018, IEEE Transactions on Smart Grid.