ReMIA offers a practical privacy metric for synthetic data by training two generators and using a classifier to detect source dataset membership, achieving sensitivity comparable to standard MIAs with far less computation.
Holdout-based empirical assessment of mixed-type synthetic data.Frontiers in big Data, 4:679939
2 Pith papers cite this work. Polarity classification is still indexing.
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EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.
citing papers explorer
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ReMIA: a Powerful and Efficient Alternative to Membership Inference Attacks against Synthetic Data Generators
ReMIA offers a practical privacy metric for synthetic data by training two generators and using a classifier to detect source dataset membership, achieving sensitivity comparable to standard MIAs with far less computation.
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EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.