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Erschienen in:
Recognition of Fuzzy or Incompletely Described Objects Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Towards Privacy-Aware Keyboards

verfasst von : Krisztian Buza, Piroska B. Kis

Erschienen in: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Verlag: Springer International Publishing

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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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Vorheriges Kapitel A Vision-Based Method for Automatic Crack Detection in Railway Sleepers
Nächstes Kapitel Saliency-Based Optimization for the Histogram of Oriented Gradients-Based Detection Methods
Fußnoten
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.
 
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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Metadaten
Titel
Towards Privacy-Aware Keyboards
verfasst von
Krisztian Buza
Piroska B. Kis
Copyright-Jahr
2018
Buch
Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Print ISBN: 978-3-319-59161-2

Electronic ISBN: 978-3-319-59162-9

Copyright-Jahr: 2018

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