Privacy at Scale in Networked Healthcare

TL;DR

This paper advocates for a comprehensive privacy-by-design approach in networked healthcare, integrating decision-theoretic differential privacy, network-aware accounting, and compliance tooling to enable trustworthy data sharing and collaboration.

Contribution

It introduces a scalable privacy framework combining privacy-by-design, decision-theoretic DP, and compliance tools, addressing practice gaps in health data privacy.

Findings

Synthesizes PET landscape in health including federated analytics and cryptography

Proposes deployable privacy agenda with privacy-budget ledgers and control plane

Highlights distributed inference as key for multi-institution learning

Abstract

Digitized, networked healthcare promises earlier detection, precision therapeutics, and continuous care; yet, it also expands the surface for privacy loss and compliance risk. We argue for a shift from siloed, application-specific protections to privacy-by-design at scale, centered on decision-theoretic differential privacy (DP) across the full healthcare data lifecycle; network-aware privacy accounting for interdependence in people, sensors, and organizations; and compliance-as-code tooling that lets health systems share evidence while demonstrating regulatory due care. We synthesize the privacy-enhancing technology (PET) landscape in health (federated analytics, DP, cryptographic computation), identify practice gaps, and outline a deployable agenda involving privacy-budget ledgers, a control plane to coordinate PET components across sites, shared testbeds, and PET literacy, to make lawful, trustworthy sharing the default. We illustrate with use cases (multi-site trials, genomics, disease surveillance, mHealth) and highlight distributed inference as a workhorse for multi-institution learning under explicit privacy budgets.