Visible to the public I Read but Don'T Agree: Privacy Policy Benchmarking Using Machine Learning and the EU GDPR

TitleI Read but Don'T Agree: Privacy Policy Benchmarking Using Machine Learning and the EU GDPR
Publication TypeConference Paper
Year of Publication2018
AuthorsTesfay, Welderufael B., Hofmann, Peter, Nakamura, Toru, Kiyomoto, Shinsaku, Serna, Jetzabel
Conference NameCompanion Proceedings of the The Web Conference 2018
PublisherInternational World Wide Web Conferences Steering Committee
Conference LocationRepublic and Canton of Geneva, Switzerland
ISBN Number978-1-4503-5640-4
Keywordsdata protection regulation, Human Behavior, privacy, Privacy Policies, privacy policy, pubcrawl, Scalability
AbstractWith the continuing growth of the Internet landscape, users share large amount of personal, sometimes, privacy sensitive data. When doing so, often, users have little or no clear knowledge about what service providers do with the trails of personal data they leave on the Internet. While regulations impose rather strict requirements that service providers should abide by, the defacto approach seems to be communicating data processing practices through privacy policies. However, privacy policies are long and complex for users to read and understand, thus failing their mere objective of informing users about the promised data processing behaviors of service providers. To address this pertinent issue, we propose a machine learning based approach to summarize the rather long privacy policy into short and condensed notes following a risk-based approach and using the European Union (EU) General Data Protection Regulation (GDPR) aspects as assessment criteria. The results are promising and indicate that our tool can summarize lengthy privacy policies in a short period of time, thus supporting users to take informed decisions regarding their information disclosure behaviors.
Citation Keytesfay_i_2018