Privacy-Preserving Cohort Analytics for Personalized Health Platforms: A Differentially Private Framework with Stochastic Risk Modeling

TL;DR

This work tackles the privacy risks of interactive, cohort-based health analytics by introducing a framework that blends deterministic cohort safeguards, differential privacy, and synthetic baselines. It advances risk management by modeling privacy loss as a stochastic process and introducing Privacy Loss at Risk ($P\text{-}VaR$) with a Monte Carlo evaluation to quantify tail privacy outcomes. The contribution includes a system architecture, a stochastic risk framework, and design guidelines that enable health platforms to balance personalized utility with rigorous privacy guarantees. The results demonstrate that distributional privacy metrics provide interpretable, decision-relevant guidance for platform designers and regulators, facilitating safer deployment of personalized population benchmarks.

Abstract

Personalized health analytics increasingly rely on population benchmarks to provide contextual insights such as ''How do I compare to others like me?'' However, cohort-based aggregation of health data introduces nontrivial privacy risks, particularly in interactive and longitudinal digital platforms. Existing privacy frameworks such as $k$-anonymity and differential privacy provide essential but largely static guarantees that do not fully capture the cumulative, distributional, and tail-dominated nature of re-identification risk in deployed systems. In this work, we present a privacy-preserving cohort analytics framework that combines deterministic cohort constraints, differential privacy mechanisms, and synthetic baseline generation to enable personalized population comparisons while maintaining strong privacy protections. We further introduce a stochastic risk modeling approach that treats re-identification risk as a random variable evolving over time, enabling distributional evaluation through Monte Carlo simulation. Adapting quantitative risk measures from financial mathematics, we define Privacy Loss at Risk (P-VaR) to characterize worst-case privacy outcomes under realistic cohort dynamics and adversary assumptions. We validate our framework through system-level analysis and simulation experiments, demonstrating how privacy-utility tradeoffs can be operationalized for digital health platforms. Our results suggest that stochastic risk modeling complements formal privacy guarantees by providing interpretable, decision-relevant metrics for platform designers, regulators, and clinical informatics stakeholders.

Figures (4)

• Figure 1: System architecture for privacy-preserving cohort analytics. User data flows through cohort assignment and aggregation layers where privacy mechanisms (k-anonymity + differential privacy) are applied. Small cohorts trigger synthetic baseline generation. The serving API delivers noisy aggregate outputs with uncertainty bounds to client applications.
• Figure 2: P-VaR distribution across parameter settings showing P-VaR$_{0.95}$, P-VaR$_{0.99}$, and CP-VaR$_{0.95}$ for all configurations. Lower values indicate better privacy protection. The recommended configuration (k=100, $\varepsilon$=0.3) achieves P-VaR$_{0.95}$ = 1.63.
• Figure 3: Privacy-utility frontier showing the tradeoff between privacy loss (P-VaR$_{0.95}$, lower is better) and utility (Spearman rank correlation, higher is better). Points colored by minimum cohort size. The Pareto frontier demonstrates that increasing cohort size improves both privacy and utility.
• Figure 4: Sensitivity analysis showing the impact of key parameters on P-VaR$_{0.95}$. Baseline configuration (k=100, $\varepsilon$=0.3, 10% adversary knowledge, 5% churn, 365 days) achieves P-VaR$_{0.95}$ = 1.63. Bars show increases (red) or decreases (green) relative to baseline. Adversary knowledge has the strongest impact on privacy risk.