Verified Extraction is introduced, an auditing framework that distinguishes identifiers attributable to fine-tuning data from spurious or prior-driven outputs and quantifies recoverable leakage under explicit query budgets.
This paper empirically evaluates ChatGPT 3.5 and 4.0 using over 23,000 real user-generated medical queries, assessing their susceptibility to privacy breaches through quasi-identifiers such as age, location, phone number and national registration number and proposes a scalable privacy evaluation model that combines k-a...
Foad Jalali, Mehran Alidoost Nia· Journal of Supercomputing· 0 citations
CAST (Concept-guided Artifact Suppression Tuning), an SAE-based framework for auditable clinical text classification, improves over its corresponding fine-tuned encoder baselines and remains competitive with strong LLM baselines, while producing a feature-level audit trail of the clinical concepts that support each pre...
This paper presents the first empirical audit of open-weight LLMs on this access-scope dimension, using the EHRSQL 2024 benchmark over MIMIC-IV, and studies default model behaviour under ordinary, benign queries.
Alberto Ballesteros-Rodríguez, Lino González-García, Miguel-Ángel Sicilia et al.· Electronics· 0 citations
De-identification of clinical notes is critical for protecting patient privacy, yet existing approaches often struggle under real-world variation and provide limited support for auditing and error analysis. By examining the outputs of NER-based systems, we observe three recurring failure modes—false positives, false ne...
Ze Zhou, Ruo-Qian Zhang, Zhicheng Jiao et al.· IEEE/ACM International Confe...· 0 citations
Substantial portions of report text are recoverable from FL gradients even at larger batch sizes and with domain-specific tokenizers, and safeguards such as secure aggregation and differential privacy are likely necessary to meet HIPAA and GDPR requirements for FL in radiology NLP.
Santhosh Parampottupadam, Andres Martinez, D. Bounias et al.· arXiv.org· 0 citations
This work introduces future querying, a paradigm that probes whether large language models can function as implicit medical world models by evaluating their ability to answer time-indexed clinical queries about a patient's future, and shows that small, locally fine-tuned open-weight models can match or approach larger...
Siri Willems, James Butterworth, L. Goetschalckx et al.· 0 citations
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