Search Results for "process"

...The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. ...

twinify is a software package for the privacy-preserving generation of a synthetic twin to a given sensitive tabular data set. On a high level, twinify follows the differentially private data-sharing process introduced by Jälkö et al.. Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI). For the latter, twinify also offers automatic modeling for easy building of models fitting the data. ...

...A data generator creates qualitative test data considering various the real life data glitches entered through various means like human data entry, voice dictation and data scanning. The data generation process is done in many steps like org data creation, data grouping, pair generation, data mutation and matching data patterns. Data generator also mangles field values of generated test data to achieve data errors and co-relate them in real life contexts like Family, Households, Organizations etc