Post-processing differentially private synthetic data

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising:

• generating, using a probability distribution of synthetic data, a first value of a utility measure function, and a second value of the utility measure function, a value of an optimization variable, the synthetic data generated from a source dataset using a first differential privacy technique, the utility measure function measuring a characteristic of a dataset;
• computing, using the value of the optimization variable, a sampling weight, the sampling weight comprising a probability of selecting a portion of data from the synthetic data;
• sampling, according to the sampling weight, the synthetic data, the sampling resulting in a sampled synthetic dataset;
• and
• training, using the sampled synthetic dataset, a machine learning model, the training resulting in a trained machine learning model.

A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:

• generating, using a probability distribution of synthetic data, a first value of a utility measure function, and a second value of the utility measure function, a value of an optimization variable, the synthetic data generated from a source dataset using a first differential privacy technique, the utility measure function measuring a characteristic of a dataset;
• computing, using the value of the optimization variable, a sampling weight, the sampling weight comprising a probability of selecting a portion of data from the synthetic data;
• sampling, according to the sampling weight, the synthetic data, the sampling resulting in a sampled synthetic dataset;
• and
• training, using the sampled synthetic dataset, a machine learning model, the training resulting in a trained machine learning model.