Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Jamila Alsayed Kassem, Tim Müller, Christopher A. Esterhuyse et al.· 0 citations
This paper examines how privacy-enhancing technologies such as synthetic data, federated learning and ‘Secure Data Environments’ can be integrated into artificial intelligence (
AI
) development processes to uphold key data protection principles in the UK
GDPR
, like storage limitation, data minimisation, purpose limitation, security, and fairness. The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical
AI
systems. The paper uses the 2015 DeepMind and Royal Free case as a practical study to realise the practical and legal benefits of privacy-enhancing technologies in medical
AI
development, particularly involving public-private collaborations. While grounded in the UK context, the findings have broader relevance to the European Union and other international jurisdictions grappling with tensions between data protection and
AI
development in the healthcare context.
Yasmine Zoya· European Journal of Health L...· 0 citations
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-anonymity, l-diversity, t-closeness, entropy, re-identification risk and delta-disclosure.
Foad Jalali, Mehran Alidoost Nia· Journal of Supercomputing· 0 citations
This study aims to guide researchers and practitioners in designing secure, efficient, and privacy-preserving AI systems for healthcare by proposing a structured classification of privacy-preserving methods into four main categories: cryptographic approaches, decentralized learning methods, perturbation-based techniques, and hybrid models.
Chaima Bejaoui, Mohamed Hadded, Hakim Ghazzai et al.· Cluster Computing· 0 citations
This work proposes a practical framework for assessing privacy risk in clinical foundation models and illustrates realistic leakage scenarios across deployment settings, map them to legal regimes, and outline complementary technical and legal mitigations.
Sana Tonekaboni, Lena Stempfle, Sasha Ronaghi et al.· 0 citations