How to De‑identify Almost Anything explores modern approaches to protecting privacy while preserving data utility across many formats. Driven by stricter regulations, rising data linkage, and AI adoption, the paper explains how statistical de‑identification hides individuals within large datasets and extends beyond structured tables to text, audio, images, and genetic data. It discusses challenges in de‑identifying unstructured and multi‑modal patient data while maintaining analytical value. As organizations increasingly rely on comprehensive, linked views of individuals for research and AI development, the paper outlines practical techniques and readiness principles to support privacy‑safe innovation in healthcare and data‑driven environments.

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