What should you do?

You have a dataset that includes confidential data. 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?
A . Decrease the value of the epsilon parameter to reduce the amount of noise added to the data
B. Increase the value of the epsilon parameter to decrease privacy and increase accuracy
C. Decrease the value of the epsilon parameter to increase privacy and reduce accuracy
D. Set the value of the epsilon parameter to 1 to ensure maximum privacy

Answer: C

Explanation:

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