Claims

PETs expand the value of data by enabling sharing where in-the-clear approaches are infeasible

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2025 · checked against its sources 2026-10-09 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q1 i? Increases in privacy can allow datasets to be shared more widely, because as internal utility is decreased, datasets meet stricter disclosure and use standards. → pets-sharing-1

Evidence

pets-sharing-1

Dorie, Vincent. (2025, December). Privacy enhancing technologies in digital learning platforms. Digital Promise. https://doi.org/10.51388/20.500.12265/278

q1 · i? · theoretical · r2

Theoretical argument in the PETs and Trust section: the article argues that privacy-preserving approaches can be politically or legally feasible where an in-the-clear record linkage may not be, so PET limitations expand the value of data. No empirical evidence is offered for this claim.

"Paradoxically, increases in privacy can allow datasets to be shared more widely: as internal utility is decreased, datasets meet stricter disclosure and use standards."

Discussion

Page metadata
Field Value
type claim
id pets-expand-data-value-through-sharing
title PETs expand the value of data by enabling sharing where in-the-clear approaches are infeasible
description PETs expand the value of data by enabling sharing where in-the-clear approaches are infeasible
status review
evidence_strength weak
newest_evidence 2025
source_checked 2026-10-09
generated by process:wiki-ingest on 2026-10-09
trust unverified — no one has confirmed this page's content
sources 1 — pets-sharing-1

Frontmatter is the page's machine-readable record — see the Schema & Guide.