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Smart Micro-Grid Systems Security and Privacy pp 145–159Cite as

Inferring Private User Behaviour Based on Information Leakage

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Part of the book series: Advances in Information Security ((ADIS,volume 71))

Abstract

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.

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Notes

  1. 1.

    β k(t) is computed as per [12].

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cape Town, Rondebosch, Cape Town, South Africa

    Pacome L. Ambassa

  2. Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany

    Anne V. D. M. Kayem & Christoph Meinel

  3. Department of Mathematics and Information Security, Royal Holloway, University of London, Egham, Surrey, UK

    Stephen D. Wolthusen

  4. Norwegian Information Security Laboratory, Gjovik University College, Norwegian University of Science and Technology, Trondheim, Norway

    Stephen D. Wolthusen

Authors
  1. Pacome L. Ambassa
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  2. Anne V. D. M. Kayem
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  3. Stephen D. Wolthusen
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  4. Christoph Meinel
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Corresponding author

Correspondence to Pacome L. Ambassa .

Editor information

Editors and Affiliations

  1. Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany

    Anne V. D. M. Kayem

  2. Department of Mathematics and Information Security, Royal Holloway, University of London, Egham, Surrey, UK

    Stephen D. Wolthusen

  3. Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam, Germany

    Christoph Meinel

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Ambassa, P.L., Kayem, A.V.D.M., Wolthusen, S.D., Meinel, C. (2018). Inferring Private User Behaviour Based on Information Leakage. In: Kayem, A., Wolthusen, S., Meinel, C. (eds) Smart Micro-Grid Systems Security and Privacy. Advances in Information Security, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-91427-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-91427-5_7

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