Automating Privacy Policy Analysis: Extracting User Interaction Data Collection Claims with BERT

In this article, the authors explore how natural language processing (NLP) can benefit consumer privacy by analyzing user interaction data collected by mobile apps. They identify a lack of transparency in app policies regarding data collection and use third-party services like Google Analytics to collect user interaction data without proper disclosure. The authors propose an approach using BERT, a powerful language model, to analyze privacy policies and classify claims related to user interaction data collection. They demonstrate the effectiveness of their approach by analyzing a dataset of 42,797 sentences from various apps.
The article highlights that even de-anonymized user interaction data can be potentially classified as personal data under regulations like GDPR, challenging the notion that anonymized data is inherently non-sensitive or non-identifiable. The authors propose a solution by automating privacy policy generation for mobile apps using BERT, which enables a thorough understanding of privacy policies and can help developers create more transparent and privacy-friendly apps.
The article concludes that NLP can play a crucial role in demystifying complex privacy policies and promoting greater transparency in the app ecosystem, ultimately benefiting consumer privacy. The authors emphasize the importance of using BERT or other powerful language models to analyze privacy policies and ensure that apps are transparent about their data collection practices. By implementing these measures, developers can create more privacy-friendly apps that prioritize users’ personal information and maintain their trust in the digital landscape.