arXiv Machine Learning
Jun 10
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.
CoRTeC: A Differentially Private Release for Synthetic Tabular Data, Decodable by Any Generator
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arXiv AI
Sep 10
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
Aug 4
Leak It: A Probabilistic Approach to Training-Data Extraction from Black-Box Language Models
arXiv:2608. 00144v1 Announce Type: new Abstract: Membership inference (MIA) on language models is usually summarised by an aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines separate members from non-members from surface text alone.
arXiv Machine Learning
Aug 12
Leak It: Per-Document Extraction Beyond Aggregate Membership Inference
arXiv:2608. 00144v2 Announce Type: replace Abstract: Membership inference (MIA) on language models is usually summarised by aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines can separate members from non-members using surface text alone.
arXiv Machine Learning
Jun 9
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
Jun 24
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.
arXiv AI
Jun 10
Advancing the State-of-the-Art in Empirical Privacy Auditing
arXiv:2606. 10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples.
arXiv AI
Jul 22
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.
arXiv Machine Learning
Jun 15
Private Prediction via PAC Privacy
arXiv:2601. 14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs.
arXiv Machine Learning
1d ago
Exploring the Privacy--Size Tradeoff for Differentially Private Self-Attentive Variational Autoencoders
arXiv:2611.05582v1 Announce Type: new Abstract: Data tends to be most useful when it is shared, and the data most worth sharing is often the data that privacy obligations restrict. Synthetic data fro...
arXiv Machine Learning
Aug 31
Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spots
arXiv:2608.27782v1 Announce Type: cross Abstract: Memorization in large language models is measured through a zoo of definitions whose formal relations are unknown, and differential privacy (DP) is t...
arXiv Machine Learning
Oct 23
Shift-aware score transport for privacy auditing without retraining
The paper introduces a shift‑aware score transport method for privacy auditing that does not require retraining the target model. It estimates the in‑distribution non‑member score distribution from out‑of‑distribution data, transports this relationship to the target covariate distribution, and applies a conservative correction to account for potential extrapolation errors. Experiments on multiple datasets with distribution shifts show that the method provides more stable privacy estimates while maintaining tighter lower bounds than existing baselines.