A privacy practitioner has recommended new techniques that add noise to the output of an AI model in order to protect data privacy. Which of the following techniques is the practitioner utilizing?

The correct answer is B. Differential privacy. Differential privacy is a mathematical technique that introduces carefully calibrated statistical noise to data outputs or model results, making it impossible to identify any individual's data while still preserving the overall accuracy of aggregate analysis. K-anonymity groups r

Privacy Architecture

Question

Options

AK-anonymity
BDifferential privacy
CModel poisoning
DPseudonymization

How the community answered

(35 responses)

B
91% (32)
C
6% (2)
D
3% (1)

Explanation

Differential privacy is a mathematical technique that introduces carefully calibrated statistical noise to data outputs or model results, making it impossible to identify any individual's data while still preserving the overall accuracy of aggregate analysis. K-anonymity groups records so individuals are indistinguishable within a set but does not add noise to model outputs. Model poisoning is an attack technique, not a privacy protection. Pseudonymization replaces direct identifiers with tokens but does not add noise to outputs.

Topics

#Differential privacy#AI privacy#Privacy-enhancing technologies#Data anonymization

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