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Python Exercise Teaches Data Minimization Using a Health-Record Filter

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A programming tutorial demonstrates the concept of data minimization by building a small Python utility that filters health records to only the fields required for a declared purpose. The exercise uses a policy dictionary mapping purposes like 'activity_summary' and 'sleep_summary' to their permitted fields, and raises an error for any unrecognized purpose — a design pattern known as fail-closed behavior. The lesson was published in the context of OpenAI's July 23, 2026 announcement of a Health feature in ChatGPT, which connects US users' medical records and Apple Health data. Three unit tests verify that the filter returns exact allowed fields, leaves the original record unchanged, and rejects unknown purposes. The tutorial targets Python 3.11 or newer and relies solely on the standard library, with all code examples marked as unexecuted.

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Python Exercise Teaches Data Minimization Using a Health-Record Filter · ShortSingh