Assessing Privacy Risks in Clinical Foundation Models: A Technical and Legal Framework
A recent study introduces a framework designed to evaluate privacy risks associated with clinical foundation models utilized for decision support and public health. The paper, titled 'Protecting patient privacy in clinical foundation models: Technical and legal perspectives,' emphasizes the dangers of model-mediated leakage, which can result in the re-identification of patients despite data-handling safeguards. The authors contend that current regulations, such as HIPAA and GDPR, offer insufficient direction regarding these indirect risks. They present scenarios of leakage, correlate them with legal frameworks, and propose both technical and legal solutions. This analysis provides a context-sensitive risk assessment aimed at protecting patient privacy while maintaining the benefits of medical foundation models. The study can be found on arXiv with the identifier 2608.07705.
Key facts
- The paper proposes a practical framework for assessing privacy risk in clinical foundation models.
- Clinical foundation models are used for decision support, screening, and public health.
- Privacy risk arises from model-mediated leakage, enabling patient re-identification.
- Existing frameworks like HIPAA and GDPR offer limited guidance for indirect threats.
- The paper illustrates realistic leakage scenarios across deployment settings.
- It maps leakage scenarios to legal regimes and outlines mitigations.
- The analysis provides a context-aware risk assessment grounded in realistic usage.
- The paper is available on arXiv under Computer Science > Artificial Intelligence.
- The arXiv identifier is 2608.07705.
Entities
Institutions
- arXiv
- HIPAA
- GDPR