arXiv AI
CIPHER: Benchmarking Cross-record Inference over Privacy-Hardened Evidence Records
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arXiv Machine Learning
Oct 2
PrivCert: Certifying Statement Support under Differential Privacy
arXiv:2609.38934v1 Announce Type: cross Abstract: Differentially private (DP) text generation can protect individual records, but privacy alone does not specify what evidence a released statement car...
arXiv Computation and Language
Sep 1
CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance
The paper introduces MIMIC-DOS, a dataset derived from MIMIC-IV that focuses on ICU cases where patient symptoms and medical signs are discordant. It presents CARE, a privacy‑compliant multi‑stage agentic reasoning framework that uses a proprietary LLM to generate structured categories and transitions, while a local LLM performs evidence acquisition and decision‑making. In retrospective evaluations on MIMIC‑DOS, CARE outperforms other LLMs and agentic workflows, demonstrating stronger handling of conflicting clinical evidence while preserving patient privacy.
arXiv Computation and Language
Aug 25
Clinically Grounded Privacy Evaluation of Medical LMs
The paper introduces a clinically grounded privacy evaluation framework for medical language models, assessing leakage across a spectrum of adversarial access levels—from publicly inferable demographics to leaked note fragments. Using this framework on an LM pretrained on 378,000 clinical notes, the authors find that routine encounter metadata leads to high verbatim memorization and significant recovery of sensitive diagnoses (e.g., AUROC 0.91 for abortion, 0.82 for HIV). They also note that exact-match memorization can overstate disclosure, with 36% of memorized tokens being templated documentation, underscoring the risks of training on longitudinal clinical data and offering a reusable evaluation tool.
arXiv Machine Learning
Oct 16
SHIP: Structured and Hierarchical Insight with Privacy
The paper introduces SHIP, a method that generates differentially private insights from textual data by mapping queries to attributes, constructing a hierarchical hybrid taxonomy, and releasing a DP synthetic table for analysis. It treats these insights as reusable representations to answer analytical queries, enabling a common basis for utility assessment. Experiments show SHIP outperforms existing private insight baselines in QA performance and structured fidelity, while highlighting remaining challenges for structured representations.
arXiv Computation and Language
Oct 19
Exploring The Empirical Privacy Landscape of Synthetic Data
The paper compares data-level and training-level methods for balancing language model capability and privacy leakage on synthetic medical records with embedded canaries. Direct finetuning achieves the highest capability but leaks the most private information, while DP‑SGD reduces leakage at a significant cost to performance. Generating privacy‑aware synthetic data—especially with uniqueness‑aware rewriting—offers a better trade‑off, and more capable rewriting models tend to lower downstream privacy risks, though gains are inconsistent with increased compute.
arXiv Machine Learning
Oct 8
Efficient Provably Private Classification with a Tabular Foundation Model
Efficient Provably Private Classification with a Tabular Foundation Model introduces PrivTab, a tabular foundation model that embeds differential privacy directly into its architecture. Pretrained on simulated datasets, PrivTab uses in-context learning to convert sensitive rows into compact, provably private summaries, enabling a single forward pass for classification. The model outperforms private linear and neural‑network baselines under moderate-to-strong privacy, shows negligible membership leakage, maintains well‑calibrated predictions, and reduces dataset fitting time by 10,000×.