Empowering User Privacy in Edge Computing: Generative AI for Dynamic Policy Compliance

Edge computing is transforming data processing by enabling real-time analytics close to data sources. However, this paradigm introduces significant privacy risks due to challenges in complying with diverse regional laws and accurately capturing users' privacy preferences, especially in the presence of multiple actors such as edge entities and third-party providers like cloud services. These challenges result in privacy violations that undermine trust and hinder the adoption of edge-based solutions. This paper identifies critical gaps in ensuring decentralized edge architectures adhere to legal requirements while addressing poorly defined privacy preferences. To address these issues, we propose a conceptual generative AI-driven framework to classify user preferences, formalize privacy policies for all actors, and dynamically align them with local regulations, making compliance detection easier. Through these contributions, this work identifies research opportunities while adhering to privacy by design principles in a conceptual framework to reduce privacy violations and enhance user trust.