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Towards Ubiquitous Privacy Decision Support: Machine Prediction of Privacy Decisions in IoT

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Convergence of Artificial Intelligence and the Internet of Things

Part of the book series: Internet of Things ((ITTCC))

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Abstract

We present a mechanism to predict privacy decisions of users in Internet of Things (IoT) environments, through data mining and machine learning techniques. To construct predictive models, we tested several different machine learning models, combinations of features, and model training strategies on human behavioral data collected from an experience-sampling study. Experimental results showed that a machine learning model called linear model and deep neural networks (LMDNN) outperforms conventional methods for predicting users’ privacy decisions for various IoT services. We also found that a feature vector, composed of both contextual parameters and privacy segment information, provides LMDNN models with the best predictive performance. Lastly, we proposed a novel approach called one-size-fits-segment modeling, which provides a common predictive model to a segment of users who share a similar notion of privacy. We confirmed that one-size-fits-segment modeling outperforms previous approaches, namely individual and one-size-fits-all modeling. From a user perspective, our prediction mechanism takes contextual factors embedded in IoT services into account and only utilizes a small amount of information polled from the users. It is therefore less burdensome and privacy-invasive than the other mechanisms. We also discuss practical implications for building predictive models that make privacy decisions on behalf of users in IoT.

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Notes

  1. 1.

    The TensorFlow implementation of LMDNN provides an API that enables programmers to selectively configure a wide, deep, or wide and deep model.

  2. 2.

    A device of ICS (\(\textit{C}_\textit{3}=6\)) takes a photo of you (\(\textit{C}_\textit{2}=11\)). This happens once (\(\textit{C}_\textit{5}=0\)), while you are in DBH (\(\textit{C}_\textit{1}=3\)), for safety purposes (\(\textit{C}_\textit{4}=1\)), namely to determine if you are a wanted criminal.

  3. 3.

    Number of respondents (scenario ID): 140 (#20), 138 (#73), 136 (#93), 162 (#111).

  4. 4.

    1—(nonzero entries/total entries in a user-scenario matrix).

  5. 5.

    Mode values of these attributes are male, 18–25, and undergraduate students, respectively.

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Acknowledgements

This research was funded by NSF Grant SES-1423629. The human subjects research described herein is covered under IRB protocol #2014-1600 at the University of California, Irvine.

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Authors and Affiliations

  1. University of California, Irvine, Irvine, CA, 92697, USA

    Hosub Lee & Alfred Kobsa

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  1. Hosub Lee
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  2. Alfred Kobsa
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Correspondence to Hosub Lee .

Editor information

Editors and Affiliations

  1. Department of Management Science and Technology, Hellenic Mediterranean University, Agios Nikolaos, Crete, Greece

    George Mastorakis

  2. Mobile Systems Laboratory (MoSys Lab), Department of Computer Science, Research Centres (UNRF) – University of Nicosia, Nicosia, Cyprus

    Constandinos X. Mavromoustakis

  3. Department of Telecommunications, Warsaw University of Technology, Warsaw, Poland

    Jordi Mongay Batalla

  4. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, Greece

    Evangelos Pallis

Appendix

Appendix

See Appendix Tables 5 and 6.

Table 5 Contextual parameters
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Table 6 Reaction parameters
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Lee, H., Kobsa, A. (2020). Towards Ubiquitous Privacy Decision Support: Machine Prediction of Privacy Decisions in IoT. In: Mastorakis, G., Mavromoustakis, C., Batalla, J., Pallis, E. (eds) Convergence of Artificial Intelligence and the Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-44907-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-44907-0_5

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