Inferring Private User Behaviour Based on Information Leakage

  • Pacome L. AmbassaEmail author
  • Anne V. D. M. Kayem
  • Stephen D. Wolthusen
  • Christoph Meinel
Part of the Advances in Information Security book series (ADIS, volume 71)


In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can provide a cost-effective power supply alternative in cases when connectivity to the national power grid is impeded by factors such as load shedding. RCSMG architectures can be designed to handle communications over a distributed lossy network in order to minimise operation costs. However, due to the unreliable nature of lossy networks communication data can be distorted by noise additions that alter the veracity of the data. In this chapter, we consider cases in which an adversary who is internal to the RCSMG, deliberately distorts communicated data to gain an unfair advantage over the RCSMG’s users. The adversary’s goal is to mask malicious data manipulations as distortions due to additive noise due to communication channel unreliability. Distinguishing malicious data distortions from benign distortions is important in ensuring trustworthiness of the RCSMG. Perturbation data anonymisation algorithms can be used to alter transmitted data to ensure that adversarial manipulation of the data reveals no information that the adversary can take advantage of. However, because existing data perturbation anonymisation algorithms operate by using additive noise to anonymise data, using these algorithms in the RCSMG context is challenging. This is due to the fact that distinguishing benign noise additions from malicious noise additions is a difficult problem. In this chapter, we present a brief survey of cases of privacy violations due to inferences drawn from observed power consumption patterns in RCSMGs centred on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.


Approximation algorithms Electrical products Home appliances Load modeling Monitoring Power demand Wireless sensor networks Distributed snapshot algorithm Micro-grid networks Monitoring Power consumption characterization Sensor networks 


  1. 1.
    A. Kayem, C. Meinel, and S. D.Wolthusen, “A smart micro-grid architecture for resource constrained environments,” in 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), March 2017, pp. 857–864.Google Scholar
  2. 2.
    M. Lisovich, D. Mulligan, and S. Wicker, “Inferring personal information from demand-response systems,” Security Privacy, IEEE, vol. 8, no. 1, pp. 11–20, Jan 2010.CrossRefGoogle Scholar
  3. 3.
    I. Rouf, H. Mustafa, M. Xu, W. Xu, R. Miller, and M. Gruteser, “Neighborhood watch: Security and privacy analysis of automatic meter reading systems,” in Proc. of the 2012 ACM Conf. on Comput. and Commun. Security, ser. CCS ’12. New York, NY, USA: ACM, 2012, pp. 462–473.Google Scholar
  4. 4.
    A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, and D. Irwin, “Private memoirs of a smart meter,” in Proc. of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, ser. BuildSys ’10. New York, NY, USA: ACM, 2010, pp. 61–66.Google Scholar
  5. 5.
    P. L. Ambassa, A. Kayem, S. D. Wolthusen, and C. Meinel, “ Secure and reliable power consumption monitoring in untrustworthy micro-grids,” in Future Network Systems and Security, ser. Communications in Computer and Information Science, R. Doss, S. Piramuthu, and W. ZHOU, Eds. Springer International Publishing Switzerland: Springer International Publishing, 2015, vol. 523, pp. 166–180. [Online]. Available:
  6. 6.
    M. Klonowski and D. Pajak, “Electing a leader in wireless networks quickly despite jamming,” in Proceedings of the 27th ACM Symposium on Parallelism in Algorithms and Architectures, ser. SPAA ’15. New York, NY, USA: ACM, 2015, pp. 304–312.Google Scholar
  7. 7.
    N. A. Lynch, Distributed Algorithms. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1996.zbMATHGoogle Scholar
  8. 8.
    P. Ambassa, S. Wolthusen, A. Kayem, and C. Meinel, “Robust snapshot algorithm for power consumption monitoring in computationally constrained micro-grids,” in IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), Nov 2015, pp. 1–6. [Online]. Scholar
  9. 9.
    S. McLaughlin, P. McDaniel, and W. Aiello, “Protecting consumer privacy from electric load monitoring,” in Proceedings of the 18th ACM Conference on Computer and Communications Security, ser. CCS ’11. New York, NY, USA: ACM, 2011, pp. 87–98. [Online]. Available:
  10. 10.
    E. Shi, T. H. H. Chan, E. G. Rieffel, R. Chow, and D. Song, “Privacy-preserving aggregation of time-series data,” in NDSS, 2011.Google Scholar
  11. 11.
    G. Ács and C. Castelluccia, “ I have a dream! (differentially private smart metering),” in Inform. Hiding, ser. Lecture Notes in Comput. Sci. Springer Berlin Heidelberg, 2011, vol. 6958, pp. 118–132.Google Scholar
  12. 12.
    J. Giraldo, A. Cardenas, and M. Kantarcioglu, “Leveraging unique cps properties to design better privacy-enhancing algorithms,” in Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp, ser. HoTSoS. New York, NY, USA: ACM, 2017, pp. 1–12. [Online]. Available:
  13. 13.
    E. Buchmann, K. Böhm, T. Burghardt, and S. Kessler, “Re-identification of smart meter data,” Personal Ubiquitous Comput., vol. 17, no. 4, pp. 653–662, Apr. 2013.CrossRefGoogle Scholar
  14. 14.
    V. Tudor, M. Almgren, and M. Papatriantafilou, “A study on data de-pseudonymization in the smart grid,” in Proceedings of the Eighth European Workshop on System Security, ser. EuroSec ’15. New York, NY, USA: ACM, 2015, pp. 2:1–2:6. [Online]. Available:
  15. 15.
    C. Dwork, “Differential privacy,” in 33rd Int. Colloq. on Automata, Languages and Programming, part II (ICALP 2006), ser. Lecture Notes in Comput. Sci., vol. 4052. Venice, Italy: Springer Verlag, July 2006, pp. 1–12.Google Scholar
  16. 16.
    P. Barbosa, A. Brito, and H. Almeida, “A technique to provide differential privacy for appliance usage in smart metering,” Information Sciences, vol. 370–371, pp. 355–367, 2016. [Online]. Available:
  17. 17.
    V. Gulisano, V. Tudor, M. Almgren, and M. Papatriantafilou, “Bes: Differentially private and distributed event aggregation in advanced metering infrastructures,” in Proceedings of the 2Nd ACM International Workshop on Cyber-Physical System Security, ser. CPSS ’16. New York, NY, USA: ACM, 2016, pp. 59–69. [Online]. Available:
  18. 18.
    J. Cortés, G. E. Dullerud, S. Han, J. Le Ny, S. Mitra, and G. J. Pappas, “Differential privacy in control and network systems,” in 2016 IEEE 55th Conference on Decision and Control (CDC), Dec 2016, pp. 4252–4272.Google Scholar
  19. 19.
    J. Giraldo, A. A. Cardenas, and M. Kantarcioglu, “Security vs. privacy: How integrity attacks can be masked by the noise of differential privacy,” in 2017 American Control Conference (ACC), May 2017, pp. 1679–1684.Google Scholar
  20. 20.
    P. L. Ambassa, A. Kayem, S. D.Wolthusen, and C. Meinel, “ Secure and reliable power consumption monitoring in untrustworthy micro-grids,” in Future Network Systems and Security, ser. Commun. in Comput. and Inform. Sci., R. Doss, S. Piramuthu, and W. ZHOU, Eds. Springer, 2015, vol. 523, pp. 166–180.Google Scholar
  21. 21.
    C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Found. Trends Theor. Comput. Sci., vol. 9, no. 3–4, pp. 211–407, Aug. 2014. [Online]. Available:
  22. 22.
    Z. Wang and M. Lemmon, “Stability analysis of weak rural electrification microgrids with droop-controlled rotational and electronic distributed generators,” in 2015 IEEE Power Energy Society General Meeting. Piscataway, NJ, USA: IEEE Press, July 2015, pp. 1–5.Google Scholar
  23. 23.
    K. Iniewski, Smart Grid: Infrastructure and Networking. New York, NY, USA: McGraw Hill, 2013.Google Scholar
  24. 24.
    A. Gómez Expósito, A. Abur, A. de la Villa Jaén, and C. Gómez-Quiles, “A multilevel state estimation paradigm for smart grids,” Proceedings of the IEEE, vol. 99, no. 6, pp. 952–976, jun 2011.Google Scholar
  25. 25.
    G. N. Korres, “A distributed multiarea state estimation,” IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 73–84, feb 2011.Google Scholar
  26. 26.
    R. Nagaraj, “Renewable energy based small hybrid power system for desalination applications in remote locations,” in 2012 IEEE 5th India International Conference on Power Electronics (IICPE). Piscataway, NJ, USA: IEEE Press, Dec 2012, pp. 1–5.Google Scholar
  27. 27.
    E. D. Moe and A. P. Moe, “Off-grid power for small communities with renewable energy sources in rural guatemalan villages,” in Global Humanitarian Technology Conference (GHTC), 2011 IEEE. Piscataway, NJ, USA: IEEE Press, Oct 2011, pp. 11–16.CrossRefGoogle Scholar
  28. 28.
    Y. Feng, C. Foglietta, A. Baiocco, S. Panzieri, and S. D. Wolthusen, “Malicious false data injection in hierarchical electric power grid state estimation systems,” in Proceedings of the Fourth International Conference on Future Energy Systems, ser. e-Energy ’13. New York, NY, USA: ACM, 2013, pp. 183–192.Google Scholar
  29. 29.
    M. Perdue and R. Gottschalg, “Energy yields of small grid connected photovoltaic system: effects of component reliability and maintenance,” IET Renewable Power Generation, vol. 9, no. 5, pp. 432–437, 2015.CrossRefGoogle Scholar
  30. 30.
    P. Buchana and T. S. Ustun, “The role of microgrids amp; renewable energy in addressing sub-saharan africa’s current and future energy needs,” in Renewable Energy Congress (IREC), 2015 6th International. Sousse, Tunisia: IEEE Press, 24–26 March 2015, pp. 1–6.Google Scholar
  31. 31.
    A. Abur and A. Gómez Expósito, Power System State Estimation: Theory and Implementation. Boca Raton, FL, USA: CRC Press, 2004.CrossRefGoogle Scholar
  32. 32.
    T. van Cutsem and M. Ribbens-Pavella, “Critical survey of hierarchical methods for state estimation of electric power systems,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, no. 10, pp. 3415–3424, oct 1983.Google Scholar
  33. 33.
    C. Gómez-Quiles, A. de la Villa Jaén, and A. Gómez Expósito, “A factorized approach to wls state estimation,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1724–1732, aug 2011.Google Scholar
  34. 34.
    A. Gómez Expósito, A. Abur, A. de la Villa Jaén, C. Gómez-Quiles, P. Rousseaux, and T. van Cutsem, “A taxonomy of multilevel state estimation methods,” Electric Power Systems Research, vol. 81, no. 4, pp. 1060–1069, apr 2011.Google Scholar
  35. 35.
    G. M. Mathews, “An optimal hierarchical algorithm for factored nonlinear weighted least squares state estimation,” in Proceedings of the 2012, 3rd IEEE PES International Conference on Innovative Smart Grid Technologies (ISGT Europe 2012). Berlin, Germany: IEEE Press, oct 2012, pp. 1–6.Google Scholar
  36. 36.
    A. Baiocco and S. D. Wolthusen, “Dynamic forced partitioning of robust hierarchical state estimators for power networks,” in Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES. Piscataway, NJ, USA: IEEE Press, Feb 2014, pp. 1–5.Google Scholar
  37. 37.
    A. Baiocco, S. Wolthusen, C. Foglietta, and S. Panzieri, “A model for robust distributed hierarchical electric power grid state estimation,” in Innovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES. Piscataway, NJ, USA: IEEE Press, Feb 2014, pp. 1–5.Google Scholar
  38. 38.
    F. D. Garcia and B. Jacobs, “Privacy-friendly energy-metering via homomorphic encryption,” in Proceedings of the 6th International Conference on Security and Trust Management, ser. STM’10. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 226–238.Google Scholar
  39. 39.
    K. Kursawe, G. Danezis, and M. Kohlweiss, “Privacy-friendly aggregation for the smart-grid,” in Proceedings of the 11th International Conference on Privacy Enhancing Technologies, ser. PETS’11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 175–191. [Online]. Available:
  40. 40.
    N. Alamatsaz, A. Boustani, M. Jadliwala, and V. Namboodiri, “Agsec: Secure and efficient cdma-based aggregation for smart metering systems,” in Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th, Jan 2014, pp. 489–494.Google Scholar
  41. 41.
    D. Kifer and A. Machanavajjhala, “No free lunch in data privacy,” in Proc. of the 2011 ACM SIGMOD Int.Conf.on Management of Data, ser. SIGMOD ’11. New York, NY, USA: ACM, 2011, pp. 193–204.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Pacome L. Ambassa
    • 1
    Email author
  • Anne V. D. M. Kayem
    • 2
  • Stephen D. Wolthusen
    • 3
    • 4
  • Christoph Meinel
    • 2
  1. 1.Department of Computer ScienceUniversity of Cape TownRondebosch, Cape TownSouth Africa
  2. 2.Hasso-Plattner-InstituteFaculty of Digital Engineering, University of PotsdamPotsdamGermany
  3. 3.Department of Mathematics and Information SecurityRoyal Holloway, University of LondonEgham, SurreyUK
  4. 4.Norwegian Information Security LaboratoryGjovik University College, Norwegian University of Science and TechnologyTrondheimNorway

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