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You have a dataset that includes confidential dat a. You use the dataset to train a model. You must use a differential privacy parameter to keep the data of individuals safe and private. You need to reduce the effect of user data on aggregated results. What should you do?
Correct Answer: C
Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy