A5066748043- Privacy-Preserving Technologies in Data
- Health disparities and outcomes
- Ethics and Social Impacts of AI
- Ethics in Clinical Research
- Artificial Intelligence in Healthcare and Education
- Data Management and Algorithms
- Nutritional Studies and Diet
- Air Quality and Health Impacts
- Workplace Health and Well-being
- AI in cancer detection
While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With health this risk may be considered double-crossing of patients' trust. Herein, we present new method to generate synthetic data individual granularity while holding privacy. Developed for sensitive biomedical the is patient-centric as it uses local model random called an "avatar data", each initial individual. This method, compared with 2 other generation techniques...
Objectives Though the rise of big data in field occupational health offers new opportunities especially for cross-cutting research, they raise issue privacy and security data, when linking sensitive from insurance, or compensation claims. We aimed to validate a large, blinded synthesized database developed CONSTANCES cohort by comparing associations between three independently selected outcomes, various exposures. Methods From cohort, large synthetic dataset was constructed using avatar...
Abstract Anonymization is crucial in the era of big data analysis. While nearly all computational methods operate on pseudonymised personal data, re-identification remains a risk. With health this risk may be considered double-crossing patients’ trust. Herein, we present new method to generate synthetic individual granularity while holding privacy. Developed for sensitive biomedical patient-centric as it uses local model random called an “avatar”, each initial individual. This applied real...