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Enhancing user awareness on inferences obtained from fitness trackers data

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Abstract

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

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Data Availability

The results of the questionnaires and the datasets analysed during the current study are available in the Zenodo repository: https://doi.org/10.5281/zenodo.6458107.

Notes

  1. https://gdpr.eu/.

  2. https://gdpr-info.eu/art-9-gdpr/.

  3. https://www.fitbit.com/global/us/legal/privacy-policy.

  4. https://privacy.commonsense.org/.

  5. https://www.garmin.com/en-US/privacy/global/.

  6. https://www.mi.com/uk/about/privacy/.

  7. https://foundation.mozilla.org/en/.

  8. https://foundation.mozilla.org/es/privacynotincluded/mi-band-5/.

  9. https://bit.ly/3TPLXgM.

  10. https://www.heart.org/en/health-topics/high-blood-pressure/the-facts-about-high-blood-pressure/all-about-heart-rate-pulse.

  11. https://gdpr-info.eu/art-4-gdpr/.

  12. https://www.cs.ucy.ac.cy/seit/.

  13. https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/special-category-data/what-is-special-category-data.

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  1. Computer Science Department, University of Cyprus, 1 University Avenue, 2109, Nicosia, Cyprus

    Alexia Dini Kounoudes & Georgia M. Kapitsaki

  2. Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417, Nicosia, Cyprus

    Ioannis Katakis

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Dini Kounoudes, A., Kapitsaki, G.M. & Katakis, I. Enhancing user awareness on inferences obtained from fitness trackers data. User Model User-Adap Inter 33, 967–1014 (2023). https://doi.org/10.1007/s11257-022-09353-8

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