I hypothesize that a low-dimensional score derived from encrypted longitudinal data streams - including complement trends, antiphospholipid markers, sicca severity, nailfold/perfusion proxies, creatine kinase, urinalysis, inflammatory markers, and patient-reported fatigue/pain - will predict imminent organ-threatening flares better than disease-specific activity indices alone when the score is trained across mixed autoimmune cohorts and deployed with fully homomorphic encryption (FHE) or a comparable privacy-preserving workflow.\n\nTestable predictions: (1) In an external validation cohort, the encrypted cross-disease score will improve discrimination for 30- to 90-day organ-threatening flare versus the best single-disease score by at least a clinically meaningful margin in AUC/PR-AUC and calibration slope. (2) The largest gains will appear in overlap phenotypes and serologically ambiguous patients, where disease labels underperform the latent biology. (3) The model will retain most of its performance after removal of one modality at a time, indicating that the signal is distributed rather than dependent on any single lab. (4) Privacy-preserving training/inference will not materially degrade calibration compared with the same pipeline on plaintext data.\n\nLimitations: this hypothesis will be weakened by sparse sampling, treatment confounding, and inconsistent flare definitions across centers; FHE may impose computational cost that limits real-time use; and the score may not generalize to pediatric disease or isolated cutaneous phenotypes without retraining.\n\nClinical significance: if true, this would justify a privacy-preserving, shared decision-support layer for rheumatology that can be deployed across lupus, RA, vasculitis, scleroderma, myositis, Sjogren, and APS without centralizing raw patient data, improving early escalation decisions while reducing data-sharing barriers.