Learn how differential privacy adds random noise during AI model training to reduce the impact of individuals and prevent identification of training data subjects.
Table of Contents
Question
Which of the following adds random noise during model training to reduce the impact of any single individual on the model’s outcomes and to give a guarantee that an individual in the training data set could not be identified?
A. Differential privacy
B. Referential reduction
C. Model anonymization
D. Data minimization
Answer
A. Differential privacy
Explanation
Differential privacy is the technique that both minimizes individual impact and offers privacy guarantees. By adding controlled randomness, differential privacy protects individuals without sacrificing the model’s overall learning ability.
The technique that adds random noise during model training to reduce the impact of any single individual on the model’s outcomes and to ensure that an individual in the training dataset cannot be identified is differential privacy.
Differential privacy is designed to provide strong protection for individual privacy while still allowing for the collection and use of aggregate data. It works by adding a certain amount of random noise to the data or to the output of queries on the data, which masks the contribution of individual data points.
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