About the Journal
ISSN: pending registration. Founding Editorial Board: appointments in progress.
The Journal of Privacy-Preserving AI in Medicine (JFPAI) is a peer-reviewed open-access journal on privacy-enhancing technologies for artificial intelligence in healthcare and biomedical research.
Clinical and biomedical AI depends on access to sensitive health data. JFPAI publishes research on methods that make such data usable for AI while protecting patients' privacy and meeting regulatory requirements.
JFPAI is published by Vallensis Publishing, Switzerland, and is intended for researchers in machine learning, clinical informatics, law and ethics. Its editorial policies follow the COPE Core Practices.
Aims & Scope
JFPAI publishes original research, reviews, and methodological contributions addressing the development, evaluation, and deployment of privacy-preserving AI systems in medical and healthcare contexts. Topics of interest include:
- Federated learning for clinical and genomic data
- Differential privacy in biomedical AI
- Secure multi-party computation in healthcare
- Homomorphic encryption for health data analysis
- Synthetic health data generation and validation
- GDPR, HIPAA, and nFADP compliance in AI systems
- Privacy risk assessment for clinical AI deployments
- De-identification and re-identification risk in medical datasets
- Trusted execution environments for sensitive health data
- Privacy-preserving genomics and biobank research
- Audit mechanisms and accountability frameworks
- Patient data sovereignty and consent management
- Cross-border health data governance
- Adversarial attacks on privacy-preserving systems
Out of scope: general privacy research without an AI component, non-healthcare AI privacy, and theoretical cryptography without a healthcare application.
