[Paper] Privacy at Scale in Networked Healthcare

Source: arXiv - 2601.04298v1

Overview

The paper “Privacy at Scale in Networked Healthcare” tackles the growing tension between the promise of connected, data‑rich health systems and the escalating risk of privacy breaches. By proposing a unified, decision‑theoretic differential‑privacy framework that spans the entire healthcare data lifecycle, the authors chart a path toward privacy‑by‑design that can be operationalized at the scale of multi‑institution collaborations.

Key Contributions

• Decision‑theoretic Differential Privacy (DP): Extends classic DP with utility‑aware budgeting, enabling explicit trade‑offs between privacy loss and clinical insight across heterogeneous health data sources.
• Network‑aware Privacy Accounting: Introduces models that capture inter‑dependencies among patients, sensors, and organizations, preventing hidden privacy leakage through correlated data streams.
• Compliance‑as‑Code Toolkit: Provides a prototype “privacy‑budget ledger” and control‑plane APIs that let health systems programmatically demonstrate regulatory due care (HIPAA, GDPR, etc.).
• Comprehensive PET Landscape Synthesis: Maps federated analytics, cryptographic computation, and DP techniques to concrete healthcare use cases, exposing gaps between research prototypes and production deployments.
• Deployable Agenda & Testbed Blueprint: Outlines a step‑by‑step rollout plan—including shared testbeds, PET literacy programs, and a coordinated control plane—for real‑world adoption.
• Illustrative Multi‑Institution Scenarios: Demonstrates how the framework supports multi‑site clinical trials, genomics consortia, disease‑surveillance networks, and mobile health (mHealth) applications.

Methodology

1. Literature & Landscape Review – Surveyed existing privacy‑enhancing technologies (PETs) used in health, categorizing them by data‑type (clinical, genomic, sensor) and deployment model (centralized, federated, cryptographic).
2. Decision‑theoretic DP Model – Built on classic ε‑DP, introduced a utility function that quantifies the clinical value of a query. An optimization routine allocates a privacy budget across a sequence of analyses to maximize expected utility while respecting a global privacy constraint.
3. Network‑Aware Accounting – Used graph‑theoretic representations of data interdependence; the model propagates privacy loss across linked nodes (e.g., a patient’s wearable data and their EMR). The total budget is adjusted to account for correlation‑induced amplification.
4. Compliance‑as‑Code Prototype – Implemented a ledger that records budget consumption, audit logs, and policy checks as code. The control plane orchestrates PET components (DP noise injection, secure aggregation, homomorphic encryption) across participating sites.
5. Use‑Case Simulations – Conducted end‑to‑end experiments on synthetic multi‑site trial data and real genomics datasets to evaluate privacy‑budget consumption, model accuracy, and compliance reporting overhead.

Results & Findings

• Utility‑Preserving: Decision‑theoretic budgeting achieved < 3 % loss in predictive performance while staying within strict privacy caps.
• Correlation‑Aware Savings: Accounting for network interdependence reduced overall ε consumption by ~20 % compared to naïve per‑node budgeting.
• Regulatory Transparency: The compliance‑as‑code ledger generated audit‑ready evidence automatically, cutting manual reporting time by an estimated 70 %.

Practical Implications

• For Developers: Control‑plane APIs expose familiar REST/gRPC endpoints for invoking DP noise addition, secure aggregation, and budget checks, making it easy to embed privacy controls directly into existing analytics pipelines.
• For Health IT Vendors: The privacy‑budget ledger can be integrated with EHR audit logs, enabling “privacy‑first” data sharing contracts that satisfy HIPAA’s “minimum necessary” rule without bespoke legal reviews for each dataset.
• For Researchers & Data Scientists: Decision‑theoretic DP provides a principled way to plan experiments—knowing exactly how much utility you’ll sacrifice for a given privacy budget—facilitating reproducible, compliant multi‑institution studies.
• For Regulators: The compliance‑as‑code approach offers a verifiable, machine‑readable artifact that demonstrates due care, potentially easing the burden of compliance audits and fostering faster approvals for data‑driven health innovations.

Limitations & Future Work

• Scalability of Secure Aggregation – Prototype handled tens of sites; performance degrades beyond a few hundred participants; optimizing cryptographic protocols remains an open challenge.
• Real‑World Deployment Validation – Experiments used synthetic and limited public datasets; large‑scale field trials in actual hospital networks are needed to assess operational overheads and stakeholder adoption.
• Dynamic Budget Management – Current budgeting assumes a static ε budget per study; future work will explore adaptive re‑allocation based on interim results and evolving regulatory constraints.
• User‑Facing Privacy Controls – Framework focuses on institutional privacy accounting; extending the model to give patients granular, consent‑driven control over their data is an important next step.

Authors

• M. Amin Rahimian
• Benjamin Panny
• James Joshi

Paper Information

• arXiv ID: 2601.04298v1
• Categories: cs.CR, cs.CY, cs.ET, cs.SE
• Published: January 7, 2026
• PDF: Download PDF