Enhanced Synthetic Data and a Unified Framework for Quantifying Privacy Risk in Synthetic Data

A system, comprising:

• a memory having, stored therein, computer program code;
• and
• one or more processing units operatively coupled to the memory and configured to execute instructions in the computer program code that cause the one or more processing units to:
• obtain a first synthetic dataset comprising a first plurality of datum;
• perform one or more privacy attacks on the first synthetic data;
• measure a success rate for each of the one or more privacy attacks;
• quantify a risk level for each of the one or more privacy attacks based, at least in part, on the respective success rate for the privacy attack;
• and
• output the quantified risk level for at least one of the one or more privacy attacks.

A computer-implemented method, comprising:

• obtaining, a first framework, a first cleartext dataset comprising a first plurality of datum;
• generating, by the framework, a first synthetic dataset based, at least in part, on the first cleartext dataset, wherein the first synthetic dataset comprises a second plurality of datum;
• applying, by the framework, at least one pseudonymization technique to at least one datum in the first synthetic dataset to generate a first enhanced synthetic dataset;
• and
• performing, by the framework, at least one analysis operation on the first enhanced synthetic dataset.