Privacy at Scale in Networked Healthcare

• URL: http://arxiv.org/abs/2601.04298v1
• Date: Wed, 07 Jan 2026 17:58:58 GMT
• Title: Privacy at Scale in Networked Healthcare
• Authors: M. Amin Rahimian, Benjamin Panny, James Joshi,
• Abstract summary: Digitized, networked healthcare promises earlier detection, precision therapeutics, and continuous care.<n>Yet, it also expands the surface for privacy loss and compliance risk.<n>We argue for a shift from siloed, application-specific protections to privacy-by-design at scale.
• Score: 1.0195618602298682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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