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  4. Establishing a Strong Baseline for Privacy Policy Classification
 
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2020
Conference Paper
Title

Establishing a Strong Baseline for Privacy Policy Classification

Abstract
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.
Author(s)
Mousavi Nejad, N.
Jabat, P.
Nedelchev, Rostislav
Scerri, Simon  
Graux, Damien  
Mainwork
ICT Systems Security and Privacy Protection. 35th IFIP TC 11 International Conference, SEC 2020. Proceedings  
Conference
International Conference on ICT Systems Security and Privacy Protection (SEC) 2020  
DOI
10.1007/978-3-030-58201-2_25
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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