Evaluation of Avatarization for Privacy-Preserving Synthetic Health Data Generation: A Case Study in Cancer...

This study evaluates the Avatar method for generating synthetic health data while preserving privacy. Using a cancer prediction dataset of 1,500 patients, we analyzed the balance between data utility and privacy protection across different parameter settings. Results show that avatars can approximate the statistical structure of the original data (utility metric: 97.96%, Hellinger distance: 0.13) while reducing re-identification risks (privacy rate: 91.7%, hidden rate: 92.4%). However, the study highlights that the choice of parameters, particularly the neighborhood size k, is critical to achieving a suitable utility-privacy trade-off. Careful tuning is therefore required before applying Avatarization in real clinical contexts.