Learning Personalized Privacy Preference from Public Data

Abstract

In the era of digital transformation, understanding personalized privacy preferences is essential for firms and policymakers to build trust and ensure compliance. Traditional methods rely on private data and explicit user input, which can be invasive and impractical. This paper introduces a novel framework that leverages public data, specifically social media posts, to predict individual privacy preferences. By employing deep learning and natural language processing, the framework extracts psychosocial traits such as lifestyle, risk preferences, and emotional states from public data, offering a nonintrusive and scalable approach. Findings reveal that psychosocial traits derived from social media provide greater predictive power than traditional private data. This model aids businesses and policymakers by offering a deeper understanding of user privacy concerns, enabling the development of effective privacy policies and practices. This innovative approach not only enhances consumer privacy control and trust but also optimizes data management for platforms and informs better regulatory decisions, showcasing the practical implications of utilizing public data for privacy preference prediction.