Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.
Citation: Sanket Desai, Nasser R Sabar, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti. Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3350-3368. doi: 10.3934/mbe.2022155
Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.
| [1] | US Energy Information Administration, Internation energy outlook 2019. Available from: https://www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf. |
| [2] | International Energy Agency, How to Guide for Smart Grids in Distribution Networks Roadmap Development and Implementation. Available from: https://www.ctc-n.org/sites/www.ctc-n.org/files/resources/technologyroadmaphow2guideforsmartgridsindistributionnetworks.pdf. |
| [3] |
S. Desai, R. Alhadad, A. Mahmood, N. Chilamkurti, A survey of privacy preserving schemes in ioe enabled smart grid advanced metering infrastructure, Cluster Comput., 22 (2019), 43–69. https://doi.org/10.1007/s10586-018-2820-9 doi: 10.1007/s10586-018-2820-9
|
| [4] |
Z. Fan, P. Kulkarni, S. Gormus, C. Efthymiou, G. Kalogridis, M. Sooriyabandara, et al., Smart grid communications: Overview of research challenges, solutions, and standardization activities, IEEE Commun. Surv. Tutorials, 15 (2013), 21–38. https://doi.org/10.1109/SURV.2011.122211.00021 doi: 10.1109/SURV.2011.122211.00021
|
| [5] | J. Zhang, Z. Chen, X. Yang, K. Chen, K. Li, Ponder over advanced metering infrastructure and future power grid, in 2010 Asia-Pacific Power and Energy Engineering Conference, (2010), 1–4. https: //doi.org/10.1109/APPEEC.2010.5448797 |
| [6] |
K. C. Armel, A. Gupta, G. Shrimali, A. Albert, Is disaggregation the holy grail of energy efficiency? the case of electricity, Energy Policy, 52 (2013), 213–234. https://doi.org/10.1016/j.enpol.2012.08.062 doi: 10.1016/j.enpol.2012.08.062
|
| [7] | N. Batra, J. Kelly, O. Parson, H. Dutta, W. Knottenbelt, A. Rogers, et al., Nilmtk: an open source toolkit for non-intrusive load monitoring, in Proceedings of the 5th international conference on Future energy systems, (2014), 265–276. https://doi.org/10.1145/2602044.2602051 |
| [8] | E. Ireland, Puerto rico smart meters believed to have been hacked – and such hacks likely to spread, 2012. Available from: https://www.smart-energy.com/regional-news/north-america/puerto-rico-smart-meters-believed-to-have-been-hacked-and-such-hacks-likely-to-spread/. |
| [9] | R. Guest, Austin police- it's not illegal, but we won't disclose it, 2007. Available from: https://www.dallascriminaldefenselawyerblog.com/austin-police-its-not-illegal/. |
| [10] | A. S. N. U. Nambi, A. R. Lua, V. R. Prasad, Loced: location-aware energy disaggregation framework, in Proceedings of the 2Nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, (2015), 45–54. https://doi.org/10.1145/2821650.2821659 |
| [11] |
L. Pereira, N. Nunes, Performance evaluation in non-intrusive load monitoring: Datasets, metrics, and tools—a review, WIREs Data Min. Knowl. Discovery, 8 (2018), e1265. https://doi.org/10.1002/widm.1265 doi: 10.1002/widm.1265
|
| [12] | V. Y. Pillitteri, T. L. Brewer, Guidelines for Smart Grid Cybersecurity, 2014. https://doi.org/10.6028/NIST.IR.7628r1 |
| [13] | J. Koo, X. Lin, S. Bagchi, Rl-blh: learning-based battery control for cost savings and privacy preservation for smart meters, in 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), (2017), 519–530. https://doi.org/10.1109/DSN.2017.16 |
| [14] | G. Giaconi, D. Gündüz, Smart meter privacy with renewable energy and a finite capacity battery, in 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), (2016), 1–5. https://doi.org/10.1109/SPAWC.2016.7536745 |
| [15] | O. Vuković, G. Dán, R. B. Bobba, Confidentiality-preserving obfuscation for cloud-based power system contingency analysis, in 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm), (2013), 432–437. https://doi.org/10.1109/SmartGridComm.2013.6687996 |
| [16] | A. R. Borden, D. K. Molzahn, P. Ramanathan, B. C. Lesieutre, Confidentiality-preserving optimal power flow for cloud computing, in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), (2012), 1300–1307. https://doi.org/10.1109/Allerton.2012.6483368 |
| [17] |
Z. Guan, G. Si, J. Wu, L. Zhu, Z. Zhang, Y. Ma, Utility-privacy tradeoff based on random data obfuscation in internet of energy, IEEE Access, 5 (2017), 3250–3262. https://doi.org/10.1109/ACCESS.2017.2662940 doi: 10.1109/ACCESS.2017.2662940
|
| [18] | S. Afrin, S. Mishra, An anonymized authentication framework for smart metering data privacy, in 2016 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), (2016), 1–5. https://doi.org/10.1109/ISGT.2016.7781185 |
| [19] |
H. Bao, L. Chen, A lightweight privacy-preserving scheme with data integrity for smart grid communications, Concurrency Comput.: Pract. Exper., 28 (2016), 1097–1110. https://doi.org/10.1002/cpe.3527 doi: 10.1002/cpe.3527
|
| [20] | A. Narayanan, V. Shmatikov, Robust de-anonymization of large sparse datasets, in 2008 IEEE Symposium on Security and Privacy (sp 2008), (2008), 111–125. https://doi.org/10.1109/SP.2008.33 |
| [21] |
L. Sweeney, K-anonymity: A model for protecting privacy, Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 10 (2002), 557–570. https://doi.org/10.1142/S0218488502001648 doi: 10.1142/S0218488502001648
|
| [22] |
Y. Yan, R. Q. Hu, S. K. Das, H. Sharif, Y. Qian, An efficient security protocol for advanced metering infrastructure in smart grid, IEEE Network, 27 (2013), 64–71. https://doi.org/10.1109/MNET.2013.6574667 doi: 10.1109/MNET.2013.6574667
|
| [23] |
S. Tonyali, K. Akkaya, N. Saputro, A. S. Uluagac, M. Nojoumian, Privacy-preserving protocols for secure and reliable data aggregation in iot-enabled smart metering systems, Future Gener. Comput. Syst., 78 (2018), 547 – 557. https://doi.org/10.1016/j.future.2017.04.031 doi: 10.1016/j.future.2017.04.031
|
| [24] | G. Ács, C. Castelluccia, I have a dream! (differentially private smart metering), Int. Workshop Inf. Hiding, (2011), 118–132. https://doi.org/10.1007/978-3-642-24178-9_9 |
| [25] | J. Zhao, T. Jung, Y. Wang, X. Li, Achieving differential privacy of data disclosure in the smart grid, in IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, (2014), 504–512. https://doi.org/10.1109/INFOCOM.2014.6847974 |
| [26] |
F. Fioretto, T. W. K. Mak, P. V. Hentenryck, Differential privacy for power grid obfuscation, IEEE Trans. Smart Grid, 11 (2020), 1356–1366. https://doi.org/10.1109/TSG.2019.2936712 doi: 10.1109/TSG.2019.2936712
|
| [27] |
D. Jeong, B. Kim, S. Dong, Deep joint spatiotemporal network (djstn) for efficient facial expression recognition, Sensors, 20 (2020), 1936. https://doi.org/10.3390/s20071936 doi: 10.3390/s20071936
|
| [28] |
J. Kim, B. Kim, P. P. Roy, D. Jeong, Efficient facial expression recognition algorithm based on hierarchical deep neural network structure, IEEE Access, 7 (2019), 41273–41285. https://doi.org/10.1109/ACCESS.2019.2907327 doi: 10.1109/ACCESS.2019.2907327
|
| [29] | Y. Yuan, J. Chu, L. Leng, J. Miao, B. Kim, A scale-adaptive object-tracking algorithm with occlusion detection, J. Image Video Proc., 2020. https://doi.org/10.1186/s13640-020-0496-6 |
| [30] | Y. Luo, X. Cai, Y. Zhang, J.Xu, X. Yuan, Multivariate time series imputation with generative adversarial networks, in 32nd Conference on Neural Information Processing Systems, Montréal, Canada, (2018), 1596–1607. |
| [31] |
N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, Y. Kim, Data synthesis based on generative adversarial networks, Proc. VLDB Endowment, 11 (2018), 1071–1083. https://doi.org/10.14778/3231751.3231757 doi: 10.14778/3231751.3231757
|
| [32] |
S. Desai, R. Alhadad, A. Mahmood, N. Chilamkurti, S. Rho, Multi-state energy classifier to evaluate the performance of the nilm algorithm, Sensors, 19 (2019), 5236. https://doi.org/10.3390/s19235236 doi: 10.3390/s19235236
|
| [33] |
X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, D. Srivastava, Dpt: Differentially private trajectory synthesis using hierarchical reference systems, Proc. VLDB Endowment, 8 (2015), 1154–1165. https://doi.org/10.14778/2809974.2809978 doi: 10.14778/2809974.2809978
|
| [34] | A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, preprint, arXiv: 1511.06434. |
| [35] |
S. Makonin, F. Popowich, I. V. Bajić, B. Gill, L. Bartram, Exploiting hmm sparsity to perform online real-time nonintrusive load monitoring, IEEE Trans. Smart Grid, 7 (2016), 2575–2585. https://doi.org/10.1109/TSG.2015.2494592 doi: 10.1109/TSG.2015.2494592
|
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Sanket Desai, Nasser R Sabar, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti. Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3350-3368. doi: 10.3934/mbe.2022155
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