Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.
The secret sharer: Evaluating and testing unintended memorization in neural networks
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.
SIGIL introduces five canary strategies and a Neyman-Pearson-based Membership Inference Score that achieves AUC 0.831-0.947 in 36,000 simulations, remaining above 0.86 even after full paraphrasing.
citing papers explorer
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Privacy Auditing with Zero (0) Training Run
Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.
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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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SynBench: A Benchmark for Differentially Private Text Generation
SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.
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Subtle Injection for Ground-truth Inference of LLM Training Data
SIGIL introduces five canary strategies and a Neyman-Pearson-based Membership Inference Score that achieves AUC 0.831-0.947 in 36,000 simulations, remaining above 0.86 even after full paraphrasing.