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SmartNoise SDK
The SmartNoise SDK is an open-source toolkit for applying differential privacy to tabular and relational data, such as spreadsheets and databases. Differential privacy protects individuals by adding carefully calibrated random noise, so that no single person's data can be identified from the results. SmartNoise lets data analysts and developers work with sensitive data using familiar tools, while providing a formal privacy guarantee. It is maintained as part of the OpenDP project and consists of two packages.
The first package, SmartNoise SQL, runs differentially private SQL queries. Analysts write standard SQL queries, for example to calculate average age by group. They also set a privacy budget, a value known as epsilon that controls the trade-off between privacy and accuracy. SmartNoise then returns results with calibrated noise added. A metadata file describes the dataset, for example which column identifies individuals, so that the tool can limit each person's influence on the results.
The second package, SmartNoise Synth, generates differentially private synthetic data. Synthetic data consists of artificial records that preserve the statistical patterns of the original dataset without reproducing real individuals. It can be shared or used to develop and test AI models where the original data could not be used. The package offers several methods, including MWEM and PATE-CTGAN, a method based on generative adversarial networks. Both packages are written in Python and released under the MIT licence.
Auto-discovered on 2026-09-23 by OECD Catalogue Automation
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