In the current landscape where data privacy intersects with the ever-growing demand for comprehensive datasets, this talk introduces a novel approach employing large language models (LLMs) for image-based editing, targeting medical images and chart image data. This technique emphasizes preserving data integrity while ensuring the utmost privacy and confidentiality. We delve into utilizing LLMs to interpret and manipulate data visualizations, including diverse chart forms like bar graphs, pie charts, and line plots, alongside medical imagery such as X-rays, MRIs, and CT scans. The LLMs discern and subtly modify particular data elements or features within these images. In chart data, this pertains to altering specific data points without skewing the overarching trends or statistical relevance. Medical imagery involves modifying or removing identifiable markers while retaining diagnostic value.

A significant aspect of our methodology is its role in data augmentation. For chart data, we generate synthetic images mirroring real data trends and enhancing datasets while adhering to privacy norms. In the realm of medical data, we create realistic, anonymized images that expand the scope of datasets, crucial in areas plagued by data scarcity, such as rare diseases or specific medical conditions.

This talk will showcase the efficacy of our approach through various case studies and experimental analyses. We will also address the ethical implications and potential constraints of using AI in this context, providing a glimpse into the future of secure data handling and augmentation in the AI era. This presentation is an invitation to explore the intersection of AI and data privacy, specifically in medical and chart data. It is a journey through the innovative ways large language models are redefining data enhancement and privacy preservation.