Privacy at Scale in Networked Healthcare

TL;DR

The paper addresses the privacy and compliance challenges of scaling data sharing in networked healthcare. It argues for privacy-by-design at scale driven by decision-theoretic differential privacy, network-aware privacy accounting, and compliance-as-code, integrating across data lifecycle and inter-institutional networks. It synthesizes the privacy-enhancing technology landscape (DP, federated analytics, cryptographic computing, synthetic data) and identifies gaps, proposing a deployable agenda with privacy-budget ledgers, a cross-site control plane, shared testbeds, and PET literacy. The result is a framework for auditable, trustworthy sharing that supports multi-site trials, genomics, disease surveillance, and mHealth, with distributed inference as a core learning engine under explicit privacy budgets.

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.