Aggregate Only the Required Events
Build daily failure counts from fictional records and drop unnecessary fields.
Supplied materials
- Paper and pencil; an adult may read aloud. No device, account or purchase is needed.
Learn the idea
Data minimization starts with a specific purpose and asks what is necessary to achieve it. Our purpose is internal daily failure counts by activity code. Individual histories, names and answer text do not help answer that count question. An aggregate reduces detail, but the context and small groups can still create privacy risks.
Fictional input records contain day, activity and outcome only: (D1,A,fail), (D1,A,pass), (D1,B,fail), (D1,A,fail), (D2,A,fail). Algorithm: start an empty count table; for each fail event, add 1 to the group keyed by (day,activity); ignore pass events; retain only the aggregate rows. Here absent groups mean zero failures, not unknown data.
- State the purpose and three permitted input fields.
- Trace the count table after each of the five supplied events.
- Return groups (D1,A), (D1,B) and (D2,A), with their counts.
- Explain why adding full_name or typed_answer is unnecessary for this purpose and why aggregate output still needs controlled access.
See the method in action
Aggregate output: (D1,A,2), (D1,B,1), (D2,A,1). Four fail events are counted and one pass event is ignored. No person identifiers or answer text are retained in the exercise output.
Check your reasoning
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See the answer and explanation
A = 1, B = 1
The new pass does not increment D2 A. The new fail creates D2 B with count 1, so both D2 groups have count 1.
Take it further
Change the purpose to daily total check counts by activity, including passes. Expected original-data counts: D1 A = 3, D1 B = 1, D2 A = 1. The aggregation condition changes; names and answers are still unnecessary for the stated totals.
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