I’m talking about perturbing the micro data enough to allow researchers to answer their questions while remaining analytically valuable. For example with schoo

I’m talking about perturbing the micro data enough to allow researchers to answer their questions while remaining analytically valuable. For example with school attendance data you could easy release a dataset at the county level with every student’s record with unique generated student id, race, grade, gender, absences by year (or even month) and still have 5-20 of each category to be able to show attendance trends before and after Covid without being able to identify individuals. And, if necessary, suppress really unique race or gender instances (eg, maybe there’s only one trans, Native American in a school) while still being useful enough to make useful findings for general trends. I don’t know what specific questions, but the state not releasing any data to them and claiming privacy is silly. The census and department of ed already do this and the department of ed has a very useful description of how they apply privacy protections and validate that data are sufficiently anonymized for public release, https://studentprivacy.ed.gov/sites/default/files/resource_document/file/data_deidentification_terms_0.pdf https://studentprivacy.ed.gov/sites/default/files/resource_d...