LATIDIA · Ciberseguridad

Fundamentos de privacidad para la inteligencia artificial científica multiinstitucional

arXiv: 2609.39787v2Announce Type: replace Resumen: La inteligencia artificial (IA) científica, que abarca desde los modelos de base (FM) hasta los canales de análisis de datos federados, se está convirtiendo en una infraestructura compartida en toda la labora nacional

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arXiv:2609.39787v2 Announce Type: replace Abstract: Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution's participation, research strategy, or technical capability rather than a single record. Differential privacy (DP), federated learning (FL), secure computation, trusted execution, and provenance each protect parts of the stack, but their guarantees rarely compose across mixed-trust institutions, access tiers, and autonomous agents. This perspective recasts privacy for scientific AI as an assurance problem defined by six elements: protected asset, observer, channel, permitted disclosure, guarantee, and evidence. We demonstrate the