Anonymization algorithms preserving result patterns may require sample sizes far larger than typical experiments¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Depending on the research type and anonymization algorithm, the sample sizes required for privacy-preserving transformation exceed those common in experimental studies. → Skulmowski 2024

Evidence¶

Skulmowski 2024¶

Skulmowski, A. (2024). AI-Related Threats to Information Sovereignty and Challenges for Research Ethics. Educational Psychology Review, 36, 101. https://doi.org/10.1007/s10648-024-09939-1

q1 · i? · theoretical · r2

The author's caution, citing prior anonymization work, that applying noise-introducing anonymization algorithms to quantitative datasets while preserving the overall result pattern demands sample sizes beyond typical experimental studies. This is an attributed statement, not a test conducted in this article.

"It needs to be noted that depending on the type of research and the algorithm used for anonymization, the sample sizes required for such a transformation are far larger than what can commonly be found in studies using experimental methods"

Discussion¶

Related Claims¶

• Machine perception and learning depend on large, unbiased training data — related
• Researchers may need to treat all remotely collected data as potentially identifiable personal data — related
• Algorithmic bias and data privacy are central ethical risks of AI in adult education — related