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TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
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Personal data collected at scale promises to improve decision-making and accelerate innovation. However, sharing and using such data raises serious privacy concerns. A promising solution is to produce synthetic data, artificial records to share instead of real data. Since synthetic records are not linked to real persons, this intuitively prevents classical re-identification attacks. However, this is insufficient to protect privacy. We here present TAPAS, a toolbox of attacks to evaluate synthetic data privacy under a wide range of scenarios. These attacks include generalizations of prior works and novel attacks. We also introduce a general framework for reasoning about privacy threats to synthetic data and showcase TAPAS on several examples.
Forward citations
Cited by 8 Pith papers
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SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
The first systematization of reconstruction attacks on synthetic tabular data finds that generator choice dominates privacy risk over attack choice, with differential privacy effective only at low budgets and most lea...
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Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting
MT-MIA uses heterogeneous graph neural networks under a No-Box model to expose user-level membership leakage in synthetic relational data that single-table attacks underestimate.
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A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities
A new framework evaluates utility of synthetic mobility trajectories while a membership inference attack reveals privacy vulnerabilities in generative models thought to be safe.
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Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.
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Quantifying the Privacy of Counterfactuals by Leveraging Membership Inference Attacks Against Synthetic Data
Membership inference attacks adapted from synthetic data succeed on counterfactuals using only the counterfactuals themselves, without model access.
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diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
diffGHOST is a conditional diffusion model that segments learned latent space to identify and mitigate memorization of critical trajectory samples, aiming to deliver privacy guarantees alongside data utility.
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Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
TabularARGN is a discretization-based auto-regressive network claimed to generate high-fidelity, privacy-robust synthetic tabular data, competitive with diffusion and GAN baselines.
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Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility
The authors apply the Adaptive Iterative Mechanism to create differentially private synthetic data from the LEMURS wearable and survey dataset and show that epsilon=5 retains useful predictive performance for downstre...
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