CDPSE: Certified Data Privacy Solutions EngineerDemo

Question

Which of the following is MOST important for helping ensure data privacy when machine learning (ML) is used to train AI models?

A Data shuffling techniques to protect privacy
B Differential privacy mechanisms to anonymize training data
C Data enrichment methods to enhance model performance
D Data validation techniques to ensure integrity

Explanation

Differential privacy introduces a formal, quantifiable privacy guarantee that limits what can be inferred about any individual training record from a model or its outputs. In contrast, data shuffling alone does not establish such a privacy guarantee, while enrichment and validation primarily address model performance and data integrity. NIST describes differential privacy as a framework for quantifying privacy loss and notes its use in privacy-preserving machine learning.

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CDPSE: Certified Data Privacy Solutions EngineerDemo

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