arXiv Machine Learning
CoRTeC: A Differentially Private Release for Synthetic Tabular Data, Decodable by Any Generator
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LLM-as-a-Discriminator: When Synthetic Tables Still Look Real
arXiv:2606. 09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension.
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PAC-Private Autoregressive Generation: Calibrating Noise to Ensemble Disagreement
The paper introduces PAC‑Private Autoregressive Generation, a method that calibrates noise based on ensemble disagreement across overlapping ‘worlds’ of a private corpus, thereby extending PAC privacy from classification to text generation. By training adapters on a frozen public model and using posterior‑weighted disagreement to add noise only when predictions vary, the approach achieves strong privacy guarantees while preserving most of the fine‑tuning benefit. Experiments on WikiText‑103 with GPT‑2‑small show 74 % of the fine‑tuning gain retained with a per‑token budget of 2⁻³², and membership‑inference success bounded to 51.08 % after one million tokens, outperforming PMixED under matched conditions.
arXiv Machine Learning
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Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models
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arXiv Machine Learning
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Leak It: Per-Document Extraction Beyond Aggregate Membership Inference
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arXiv Machine Learning
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SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
arXiv:2606. 08372v1 Announce Type: cross Abstract: Synthetic data is increasingly promoted as a privacy-preserving substitute for releasing sensitive tabular records, yet its central adversarial threat ("reconstruction", the recovery of an individual's hidden attribute values from a synthetic release and a handful of known quasi-identifiers) has been studied only in scattered, hard-to-compare settings.
arXiv Machine Learning
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Natural Identifiers for Privacy and Data Audits in Large Language Models
arXiv:2606. 24408v1 Announce Type: new Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges.