Privacy-Preserving Location-Based Services: A DQN Algorithmic Perspective

With the increasing prevalence of location-based services, preserving user privacy has become a paramount concern. In this research, we propose a comprehensive privacy-preserving approach using Deep-Q networks which uses the best property of both reinforcement learning and deep learning models. The model intelligently gathers contextual information, such as app category, user frequency, and context, to accurately predict user's privacy behavior towards location data access requests. To further enhance privacy protection, we incorporate an obfuscated region technique. The combination of these techniques empowers users with personalized privacy preferences while safeguarding sensitive data. Additionally, we address potential adversarial attacks through adversarial training and differential privacy. Our approach contributes to a more privacy-centric data landscape, allowing users to make informed decisions about their data sharing in today's data-driven world.