Privacy and customer segmentation in the smart grid

In the electricity grid, networked sensors which record and transmit increasingly high-granularity data are being deployed. In such a setting, privacy concerns are a natural consideration. In order to obtain the consumer's valuation of privacy, we design a screening mechanism consisting of a menu of contracts offered to the energy consumer with varying guarantees of privacy. The screening process is a means to segment customers. Finally, we design insurance contracts using the probability of a privacy breach to be offered by third-party insurance companies.

[1]  Georgios Kalogridis,et al.  Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[2]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

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

[4]  George J. Pappas,et al.  Differentially Private Filtering , 2012, IEEE Transactions on Automatic Control.

[5]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[6]  Duncan S. Callaway,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013, IEEE Transactions on Power Systems.

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

[8]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[9]  Anthony Rowe,et al.  BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research , 2012 .

[10]  S. Rosen,et al.  Monopoly and product quality , 1978 .

[11]  Henrik Ohlsson,et al.  Energy disaggregation via adaptive filtering , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[12]  G. Jaynes Equilibria in monopolistically competitive insurance markets , 1978 .

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

[14]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[15]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[16]  Alex Rogers,et al.  Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types , 2012, AAAI.

[17]  Henrik Ohlsson,et al.  Fundamental limits of nonintrusive load monitoring , 2013, HiCoNS.

[18]  Annabelle Lee,et al.  Guidelines for Smart Grid Cyber Security , 2010 .

[19]  Henrik Ohlsson,et al.  Quantifying the Utility-Privacy Tradeoff in the Smart Grid , 2014, ArXiv.

[20]  Joseph E. Stiglitz,et al.  Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information , 1976 .

[21]  Hideaki Ishii,et al.  Hinfinity control with limited communication and message losses , 2008, Syst. Control. Lett..

[22]  Elias Leake Quinn,et al.  Smart Metering and Privacy: Existing Laws and Competing Policies , 2009 .

[23]  P. Dasgupta,et al.  Equilibrium in Competitive Insurance Markets : An Essay on the Economics of Imperfect Information , 2007 .

[24]  Bashar Nuseibeh,et al.  Adaptive security and privacy in smart grids: A software engineering vision , 2012, 2012 First International Workshop on Software Engineering Challenges for the Smart Grid (SE-SmartGrids).

[25]  Matthew J. Johnson,et al.  Bayesian nonparametric hidden semi-Markov models , 2012, J. Mach. Learn. Res..

[26]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[27]  S. Shankar Sastry,et al.  A game theory model for electricity theft detection and privacy-aware control in AMI systems , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[28]  Thomas A. Weber Optimal Control Theory with Applications in Economics , 2011 .