How is Gretel-synthetics differential privacy different from traditional implementations?

Several companies including Uber have built libraries that help apply differential privacy to SQL queries, by injecting noise into the results of a query aggregation. This approach is powerful but requires you to know what questions that you want to ask of data, without the ability to see or inspect sensitive data directly. Gretel-synthetics is a sequence-to-sequence model that trains on a source dataset, injects noise during the learning process rather than at query time, and creates a secondary dataset that can be shared and viewed directly by data scientists or developers or queried using any database technology.

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