New method quantifies privacy risk in synthetic tabular data

Researchers have developed a new method to quantify the risk of membership disclosure in tabular synthetic data using kernel density estimators (KDEs). This approach models the distances between synthetic and training data points to probabilistically infer membership, offering a more robust evaluation than previous methods. The proposed technique, demonstrated on real-world datasets, provides a practical framework for data custodians to assess and characterize privacy risks before releasing synthetic datasets. AI

IMPACT Provides a new metric for assessing privacy risks in synthetic data, crucial for sensitive domains like healthcare and finance.

RANK_REASON The cluster contains an academic paper detailing a new method for quantifying privacy risks in synthetic data. [lever_c_demoted from research: ic=1 ai=1.0]

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• Hugging Face
• Membership Inference Attacks
• Rajdeep Pathak
• Realistic Attack
• tabular synthetic data
• True Distribution Attack

• paper
• safety

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