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Towards Privacy-Aware Keyboards

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Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

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Abstract

As shown by various studies, the dynamics of typing on a keyboard is characteristic to persons. On the one hand, this may allow for person identification based on keystroke dynamics in various applications. On the other hand, in certain situations, such as chat-based anonymous helplines, web search for sensitive topics, etc., users may not want to reveal their identity. In general, there are various methods to increase the protection of personal data. In this paper, we propose the concept of privacy-aware keyboard, i.e., a keyboard which transmits keyboard events (such as pressing or releasing of a key) with small random delays in order to ensure that the identity of the user is difficult to be inferred from her typing dynamics. We use real-world keystroke dynamics data in order to simulate privacy-aware keyboards with uniformly random delay and Gaussian delay. The experimental results indicate that the proposed techniques may have an important contribution to keeping the anonymity of users.

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Notes

  1. 1.

    The number of typing sessions was approximately the same for each user. Despite the fact that the data is balanced, the recognition of the user based on typing dynamics could lead to an imbalanced classification task, for example in case if binary classifiers are used according to the one-vs-rest schema.

  2. 2.

    http://biointelligence.hu/typing-challenge/.

  3. 3.

    With random guessing we mean a naive classifier that works as follows: for each typing pattern x of the test data, is selects one of the users randomly (each user has an equal probability to be selected), and this randomly selected user, denoted as \(y_x^{(rnd)}\), is the prediction of the classifier. That is: according to the “guess” of this naive classifier, the typing pattern x belongs to the randomly selected user \(y_x^{(rnd)}\). As there are 12 users in our dataset, with a probability of 1 / 12 the randomly selected user will match the true user associated with the typing pattern, therefore, the accuracy of random guessing is 1 / 12.

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Authors and Affiliations

  1. Knowledge Discovery and Machine Learning, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    Krisztian Buza

  2. University of Dunaujvaros, Dunaujvaros, Hungary

    Piroska B. Kis

Authors
  1. Krisztian Buza
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  2. Piroska B. Kis
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Correspondence to Krisztian Buza .

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Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

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Buza, K., Kis, P.B. (2018). Towards Privacy-Aware Keyboards. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-59162-9_15

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  • Online ISBN: 978-3-319-59162-9

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