Enhancing privacy protection of physical examination data through synthetic algorithms based on differential privacy.

ABSTRACT:

Background

Health physical examinations play a crucial role in early detection of cancer and chronic disease. However, privacy concerns limit the utilization of this kind of data for health interventions and research. Synthetic data methods based on differential privacy are increasingly used to create complete datasets that protect privacy while enabling data analysis and result interpretation. Hence, the use of synthetic algorithms based on differential privacy for privacy protection of physical examination data is a promising research direction.

Methods

Three synthetic algorithms, PrivBayes, PeGS, and DP-Gibbs were used to generate complete synthetic datasets that adhere to differential privacy standards using physical examination data composed of categorical data, which c

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12403299 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature