Explainable Machine Learning for Default Privacy Setting Prediction

Löbner, S.; Tesfay, W. B.; Nakamura, T. and Pape, S.

In IEEE Access, 9: 63700-63717, 2021.

Abstract

When requesting a web-based service, users often fail in setting the website's privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well suited. Such tools aim to lower the burden to set privacy preferences according to owners privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations - such as the General Data Protection Regulation (GDPR) in Europe - this paper introduces an explainable model for default privacy setting prediction. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model's design before they go into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.

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Bibtex

@Article{LTNP21access,
  author   = {Sascha L{\"o}bner and Welderufael B. Tesfay and Toru Nakamura and Sebastian Pape},
  title    = {Explainable Machine Learning for Default Privacy Setting Prediction},
  journal  = {IEEE Access},
  year     = {2021},
  volume   = {9},
  pages    = {63700--63717},
  month    = {04},
  doi      = {10.1109/ACCESS.2021.3074676},
  keywords = {privacy, machine learning, CS4E, select},
  url      = {https://ieeexplore.ieee.org/document/9410256},
}

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