SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data

• URL: http://arxiv.org/abs/2601.12124v1
• Date: Sat, 17 Jan 2026 17:51:14 GMT
• Title: SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data
• Authors: Bing Hu, Yixin Li, Asma Bahamyirou, Helen Chen,
• Abstract summary: We introduce SynQP, an open framework for privacy benchmarking in synthetic data generation.<n>We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models.<n>Our work provides a critical tool for improving the transparency and reliability of privacy evaluations.
• Score: 4.73374389278596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the absence of accessible benchmark datasets for evaluating privacy risks, due to difficulties in acquiring sensitive data. To address this, we introduce SynQP, an open framework for benchmarking privacy in synthetic data generation (SDG) using simulated sensitive data, ensuring that original data remains confidential. We also highlight the need for privacy metrics that fairly account for the probabilistic nature of machine learning models. As a demonstration, we use SynQP to benchmark CTGAN and propose a new identity disclosure risk metric that offers a more accurate estimation of privacy risks compared to existing approaches. Our work provides a critical tool for improving the transparency and reliability of privacy evaluations, enabling safer use of synthetic data in health-related applications. % In our quality evaluations, non-private models achieved near-perfect machine-learning efficacy \(\ge0.97\). Our privacy assessments (Table II) reveal that DP consistently lowers both identity disclosure risk (SD-IDR) and membership-inference attack risk (SD-MIA), with all DP-augmented models staying below the 0.09 regulatory threshold. Code available at https://github.com/CAN-SYNH/SynQP

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