Automated generation of privacy policy using deep models

Abstract

Personal information protection and compliance with privacy regulations are becoming increasingly important due to a large number of security and privacy breaches. These privacy breaches can harm individuals in both personal and social contexts. Privacy policies are the primary means of communication with which service providers could inform users about the data collection and sharing practices. The content and transparency of these legal documents are of importance as they can help users make decisions about the service providers’ data privacy practices and can build trust with the users. Although many regulations and best practices have provided recommendations and guidelines on the content of privacy policies, research has shown that the content of these documents is usually incomplete and miss important topics. To address this issue, we propose and validate the use of automated generative models for creating the content of privacy policies. These generative approaches use deep learning models to generate enriched data practices and text for privacy policies. In this study, we trained two generative models (Long Short-Term Memory (LSTM) and bidirectional Long Short-Term Memory bi-LSTM) on annotated privacy policies to automatically generate privacy data practices. Training and testing were performed on three levels of paragraph, sentence, and data practice. Our findings have shown promising results and have suggested that models trained on legal data practices using bi-LSTM algorithm create more accurate results.