How does Gretel-synthetics leverage differential privacy?

Gretel-synthetics uses differential privacy to defend against memorization while learning on a private dataset. Imprecisely speaking, the output of a synthetic model trained over a dataset D that contained one occurrence of a secret training record X versus another synthetic model D1 that did not contain X should be nearly identical. Thus, we have mathematical assurances that our model did not memorize the secret.

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