Aim high, stay private: differentially private synthetic data enables public release of behavioral health information...

Sharing behavioral health and wearable data poses privacy challenges, as traditional de-identification remains vulnerable to re-identification. Differential privacy (DP) provides mathematical guarantees through a tunable privacy budget, $ϵ$ . This study evaluates the feasibility of generating and releasing DP synthetic behavioral health data with high analytical utility, identifying practical $ϵ$ values for public data sharing.

We analyzed physiological data from wearable devices and self-reported data from Phase 1 of the Lived Experiences Measured Using Rings Study (LEMURS), which tracked sleep, stress, and well-being among first-year college students. Three DP synthetic data generators: AIM, MST, and PATECTGAN, were evaluated across privacy budgets ranging from $ϵ=1$ to 100. Utility was assessed using L1/L2 errors, correlation, regression, UMAP, and assessed vulnerability via privacy attacks.

AIM outperformed MST and PATECTGAN in preserving both statistical and analytical properties of the original data. For the Survey dataset, the lowest marginal errors occurred at $ϵ=5$ and 10. Correlation, regression, and UMAP analyses confirmed that AIM-generated data closely replicated original relationships at moderate $ϵ$ values.

Choice of privacy budget is still an open question, and it is task-agnostic and dataset-specific. Moderate privacy budgets ( $5\le ϵ\le 10$ ) maintained key associations between physiological and psychological measures while ensuring privacy. AIM's workload-aware design effectively allocated noise toward relevant features, enhancing performance.

A privacy budget of $ϵ=5$ offers a practical balance between data utility and participant privacy for LEMURS behavioral health data sharing.