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

2026-01-17

AI Summary

This work addresses the escalating re-identification risks in personalized health platforms, where interactive and longitudinal use of cohort data leads to cumulative, distributional, and tail-dominated privacy threats that existing methods struggle to capture. To tackle this challenge, the authors propose a privacy-preserving analytical framework that integrates deterministic cohort constraints, differential privacy, and synthetic data generation. A key innovation is the introduction of stochastic risk modeling, which treats re-identification risk as a time-varying random variable and employs Monte Carlo simulation to characterize its distribution. Drawing inspiration from financial risk metrics, the study defines Privacy Value-at-Risk (P-VaR)—a novel measure that quantifies worst-case privacy loss—thereby offering an interpretable foundation for system design and regulatory oversight. Empirical evaluations demonstrate that the framework significantly enhances practical privacy operability and assessment accuracy while preserving data utility.

Technology Category

• Machine Learning: Privacy
• Reasoning under Uncertainty: Stochastic Optimization
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• privacy-preserving
• cohort analytics
• re-identification risk
• differential privacy
• personalized health

Innovation

• differential privacy
• stochastic risk modeling
• cohort analytics
• Privacy Loss at Risk
• synthetic baseline generation