Public articles linked to the same research event.
bioRxiv The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f
The authors introduce SPHERE, a model-free method that turns sensitive datasets into shareable synthetic twins that AI systems and collaborators can use directly while the original records never leave the local environment; across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving statistical structure (means, variances and correlations reproduced exactly, effect size and P value numerically identical in linear analysis, nonlinear machine-learning utility retained, each twin generated in seconds on a laptop), frontier AI agents on the twin reach the same scientific conclusions as on the original records, analyses reproduce genome- and proteome-wide results at UK Biobank scale and recover landmark f