CDPSE · Question #390
Which of the following MOST effectively ensures data privacy when sharing datasets for machine learning (ML) model training?
The correct answer is B. Data anonymization. Anonymization (de-identification) is the PET that removes or irreversibly transforms identifiers so individuals are not identifiable, enabling safer secondary use and sharing. Controls like encryption in transit (D) and attribute-based access (C) restrict access or protect data…
Question
Which of the following MOST effectively ensures data privacy when sharing datasets for machine learning (ML) model training?
Options
- AIntegrity checks
- BData anonymization
- CAttribute-based access
- DData encryption in transit
How the community answered
(38 responses)- A16% (6)
- B74% (28)
- C3% (1)
- D8% (3)
Explanation
Anonymization (de-identification) is the PET that removes or irreversibly transforms identifiers so individuals are not identifiable, enabling safer secondary use and sharing. Controls like encryption in transit (D) and attribute-based access (C) restrict access or protect data in motion but do not prevent reidentification once data are accessed. Integrity checks (A) protect correctness, not privacy. Key CDPSE-aligned phrasing (short extract): "Anonymization... renders personal data not identifiable to a data subject."
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