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Establishing a Strong Baseline for Privacy Policy Classification

: Mousavi Nejad, N.; Jabat, P.; Nedelchev, R.; Scerri, S.; Graux, D.


Hölbl, Marko ; International Federation for Information Processing -IFIP-:
ICT Systems Security and Privacy Protection. 35th IFIP TC 11 International Conference, SEC 2020. Proceedings : Maribor, Slovenia, September 21-23, 2020
Cham: Springer Nature, 2020 (IFIP advances in information and communication technology 580)
ISBN: 978-3-030-58200-5 (Print)
ISBN: 978-3-030-58201-2 (Online)
ISBN: 978-3-030-58202-9
ISBN: 978-3-030-58203-6
International Conference on ICT Systems Security and Privacy Protection (SEC) <35, 2020, Maribor>
Fraunhofer IAIS ()

Digital service users are routinely exposed to Privacy Policy consent forms, through which they enter contractual agreements consenting to the specifics of how their personal data is managed and used. Nevertheless, despite renewed importance following legislation such as the European GDPR, a majority of people still ignore policies due to their length and complexity. To counteract this potentially dangerous reality, in this paper we present three different models that are able to assign pre-defined categories to privacy policy paragraphs, using supervised machine learning. In order to train our neural networks, we exploit a dataset containing 115 privacy policies defined by US companies. An evaluation shows that our approach outperforms state-of-the-art by 5% over comparable and previously-reported F1 values. In addition, our method is completely reproducible since we provide open access to all resources. Given these two contributions, our approach can be considered as a strong baseline for privacy policy classification.