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Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

The advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring (NILM). The development of robust AI and machine learning algorithms based on deep learning architecture has enabled accurate extraction of individual appliance load signature from aggregated energy data. However, the success rate of NILM algorithm in disaggregating individual appliance load signature in smart grid data violates the privacy of the individual household lifestyle. This paper investigates the performance of Sequence-to-Sequence (Seq2Seq) deep learning NILM algorithm in predicting the load signature of appliances. Furthermore, we define a new notion of disclosure risk to understand the risk associated with individual appliances in aggregated signals. Two publicly available energy disaggregation datasets have been considered. We simulate three inference attack scenarios to better ascertain the risk of publishing raw energy data. In addition, we investigate three activation extraction methods for appliance event detection. The results show that the disclosure risk associated with releasing smart grid data in their original form is on the high side. Therefore, future privacy protection mechanisms should devise efficient methods to reduce this risk.

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References

  1. Ibrahim, M., El-Zaart, A., Adams, C.: Smart sustainable cities roadmap: readiness for transformation towards urban sustainability. Sustain. Urban Areas 37, 530–540 (2018)

    Google Scholar 

  2. Gopinath, R., Kumar, M., Joshua, C.P.C., Srinivas, K.: Energy management using non-intrusive load monitoring techniques-state-of-the-art and future research directions. Sustain. Urban Areas 62(2020), 102411 (2020)

    Google Scholar 

  3. Janik, A., Ryszko, A., Szafraniec, M.: Scientific landscape of smart and sustainable cities literature: a bibliometric analysis. Sustainability 12(3), 779 (2020)

    Article  Google Scholar 

  4. Lin, X., Tian, Z., Lu, Y., Niu, J., Cao, Y.: An energy performance assessment method for district heating substations based on energy disaggregation. Energy Build. 255, 111615 (2022)

    Article  Google Scholar 

  5. Batra, N., et al.: Towards reproducible state-of-the-art energy disaggregation. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 193–202. ACM (2019)

    Google Scholar 

  6. Precioso, D., Gomez-Ullate, D.: NILM as a regression versus classification problem: the importance of thresholding. arXiv preprint arXiv:2010.16050 (2020)

  7. Laviron, P., Dai, X., Huquet, B., Palpanas, T.: Electricity demand activation extraction: from known to unknown signatures, using similarity search. In: Proceedings of the ACM International Conference on Future Energy Systems, e-Energy. ACM (2021)

    Google Scholar 

  8. Kelly, J., Knottenbelt, W.: Neural NILM: deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, pp. 55–64. ACM (2015)

    Google Scholar 

  9. Desai, S., Alhadad, R., Mahmood, A., Chilamkurti, N., Rho, S.: Multi-state energy classifier to evaluate the performance of the NILM algorithm. Sensors 19(23), 5236 (2019)

    Article  Google Scholar 

  10. Zhang, C., Zhong, M., Wang, Z., Goddard, N., Sutton, C.: Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32. AAAI (2018)

    Google Scholar 

  11. Mashima, D., Serikova, A., Cheng, Y., Chen, B.: Towards quantitative evaluation of privacy protection schemes for electricity usage data sharing. ICT Express 4(1), 35–41 (2018)

    Article  Google Scholar 

  12. Tudor, V., lmgren, M., Papatriantafilou, M.: A study on data de-pseudonymization in the smart grid. In: Proceedings of the Eighth European Workshop on System Security, pp. 1–6 (2015)

    Google Scholar 

  13. Armoogum, S., Bassoo, V.: Privacy of energy consumption data of a household in a smart grid. In: Yang, Q., Yang, T., Li, W. (eds.) Smart Power Distribution Systems, pp. 163–177. Academic Press (2019)

    Google Scholar 

  14. BBCNews, “Ukraine power cut ‘was cyber-attack”’ (2017). https://www.bbc.com/news/technology-38573074

  15. Chin, J.-X., De Rubira, T.T., Hug, G.: Privacy-protecting energy management unit through model-distribution predictive control. IEEE Trans. Smart Grid 8(6), 3084–3093 (2017)

    Article  Google Scholar 

  16. Jia, R., Sangogboye, F.C., Hong, T., Spanos, C., Kjærgaard, M.B.: PAD: protecting anonymity in publishing building related datasets. In: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, pp. 1–10 (2017)

    Google Scholar 

  17. Sangogboye, F.C., Jia, R., Hong, T., Spanos, C., Kjærgaard, M.B.: A framework for privacy-preserving data publishing with enhanced utility for cyber-physical systems. ACM Trans. Sens. Netw. (TOSN) 14(3–4), 1–22 (2018)

    Google Scholar 

  18. Soykan, E.U., Bilgin, Z., Ersoy, M.A., Tomur, E.: Differentially private deep learning for load forecasting on smart grid. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE (2019)

    Google Scholar 

  19. Thouvenot, V., Nogues, D., Gouttas, C.: Data-driven anonymization process applied to time series. In: SIMBig, pp. 80–90 (2017)

    Google Scholar 

  20. Fioretto, F., Van Hentenryck, P.: Differential private stream processing of energy consumption. arXiv preprint arXiv: 1808.01949 (2018)

  21. Hart, G.W., Kern Jr., E.C., Schweppe, F.C.: Non-intrusive appliance monitor apparatus, 15 August 1989. US Patent 4,858,141

    Google Scholar 

  22. Çimen, H., Bazmohammadi, N., Lashab, A., Terriche, Y., Vasquez, J.C., Guerrero, J.M.: An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring. Appl. Energy 307, 118136 (2022)

    Article  Google Scholar 

  23. Feng, X., Lan, J., Peng, Z., Huang, Z., Guo, Q.: A novel privacy protection framework for power generation data based on generative adversarial networks. In: 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–5. IEEE (2019)

    Google Scholar 

  24. Khwaja, A.S., Anpalagan, A., Naeem, M., Venkatesh, B.: Smart meter data obfuscation using correlated noise. IEEE Internet Things J. 7(8), 7250–7264 (2020)

    Article  Google Scholar 

  25. Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  26. Kelly, J., Knottenbelt, W.: The UK-dale dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2(1), 1–14 (2015)

    Article  Google Scholar 

  27. Murray, D., Stankovic, L., Stankovic, V.: Refit: Electrical load measurements (cleaned) (2016). https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements-cleaned

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Acknowledgement

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. The first author is supported by the Kempe foundation.

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Authors and Affiliations

  1. Department of Computing Science, Umeå University, Umeå, Sweden

    Kayode Sakariyah Adewole & Vicenç Torra

  2. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Kayode Sakariyah Adewole

Authors
  1. Kayode Sakariyah Adewole
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  2. Vicenç Torra
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Corresponding author

Correspondence to Kayode Sakariyah Adewole .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Adewole, K.S., Torra, V. (2022). Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_6

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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