Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning
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Abstract (arXiv)
Since GDPR came into force in May 2018, companies have worked on their data practices to comply with this privacy law. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. Therefore, it is unclear if they comply with GDPR. In this paper, we create a privacy policy dataset of 1,080 websites labeled with the 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model which can classify the privacy policies with an accuracy of 89.2%. We apply our model to perform a measurement on the compliance in the privacy policies. Our results show that even after GDPR went into effect, 97% of websites still fail to comply with at least one requirement of GDPR.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 8 Nov 2021.
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References · 9
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
- 2018Deep contextualized word representationscited by 2,290
- 2017Deep Active Learning for Named Entity Recognitioncited by 108
- 2018Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learningcited by 50
- 2014Effective Use of Word Order for Text Categorization with Convolutional Neural Networkscited by 40
- 2018The Privacy Policy Landscape After the GDPRcited by 18
- 2019Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Modelscited by 16
- 2018We Value Your Privacy ... Now Take Some Cookies: Measuring the GDPR's Impact on Web Privacycited by 15
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