sukjingitsit/PrivSyn
An open-source implementation of PrivSyn: Differentially Private Data Synthesis (USENIX Security Conference, 21)
This project helps data privacy officers, researchers, or data stewards create synthetic datasets from sensitive real-world data like health records or demographic information. It takes your raw tabular data and configuration files, processes them to add differential privacy, and outputs a synthetic dataset that protects individual privacy while retaining statistical properties. This allows you to share data for analysis or development without exposing private information.
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Use this if you need to share or analyze sensitive tabular data while rigorously protecting individual privacy through differential privacy.
Not ideal if you need a quick, no-configuration solution for simple data anonymization or if your data is not tabular.
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Jun 02, 2024
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