Mirroring Privacy Risks with Digital Twins: When Pieces of Personal Data Suddenly Fit Together

Abstract

With the proliferation of social media, more personal information is being shared online than ever before, raising significant privacy concerns. This paper presents a novel approach to identify and mitigate privacy risks by generating digital twins from social media data. We propose a comprehensive framework that includes data collection, processing, and analysis, with special attention to data standardization, pseudonymization, and the use of synthetic data to ensure privacy compliance. We apply and evaluate state-of-the-art techniques such as Large Language Models, Generative Adversarial Networks, and Vision-Language Models to generate synthetic but realistic social media data that support the construction of accurate and representative digital twins while ensuring strict privacy compliance. Our approach demonstrates the potential for digital twins to help identify and mitigate privacy risks associated with social media use. We discuss the value and feasibility of this concept and suggest that further refinement of the techniques and conditions involved is needed.