Adapting Geo-Indistinguishability for Privacy-Preserving Collection of Medical Microdata

In the era of Fourth Industrial Revolution, increasing demand for data collection and sharing analysis purposes has raised concerns regarding privacy violations. Protecting individual during dissemination sensitive information emerged as a critical concern. this paper, we propose privacy-preserving framework collecting users’ medical microdata, utilizing geo-indistinguishability (Geo-I), concept based on well-known differential privacy. We adapt Geo-I, originally designed protecting location privacy, to collect microdata while minimizing reduction in utility. To mitigate utility caused by perturbation mechanism novel technique that utilizes prior distribution being collected. The proposed enables perturbed with similar original dataset, even scenarios high levels protection, typically requiring significant perturbations data. evaluate performance our algorithms using real-world demonstrate approach significantly outperforms existing methods, ensuring user preserving collection.