Pre-Registered Validation of Bilateral Composition III Privacy-Preserving Architecture for Surgical-Robot Patient-Pair Digital Twins-- A 78-Study Campaign

This deposit contains the complete pre-registration, runner code, summary verdicts, per-study reports, figures, and arXiv-format manuscript for a 78-study pre-registered empirical validation campaign of a bilateral Composition III privacy-preserving architecture for surgical-robot patient-pair digital-twin deployments. The campaign comprises approximately 425 pre-registered hypotheses across six batches, executed under continuous deterministic protocol (PYTHONHASHSEED=0) with zero post-hoc threshold adjustments. Approximately 75% of pre-registered hypotheses are SUPPORTED; remaining outcomes are honestly disclosed as bounded-negative findings that characterize deployment boundaries. The architecture extends the companion Paper 8 (10.5281/zenodo.20466035) from single-channel-group musculoskeletal state to bilateral patient-and-robot state. Key empirical findings: (i) Composition III strictly dominates the Laplace and Gaussian differential-privacy mechanisms at every tested privacy budget (patient AUC 0.468 vs Laplace 0.591 and Gaussian 0.533 at ε=1.0, with task classification accuracy 1.000 vs 0.375 and 0.271 respectively); (ii) mutual information between protected output and patient identity equals 0.000 bits; (iii) the formal Rényi-DP bound is approximately 40× looser than the empirically-audited ε; (iv) GDPR Article 17 right-to-be-forgotten is O(1)-computable with statistical indistinguishability from a never-trained model; (v) the architecture scales cleanly to 1000-patient cohorts and to multi-arm da Vinci configurations; (vi) fresh execution on real KIMORE rehabilitation skeleton data confirms architecture function on real biological human kinematic data. The deposit includes 78 pre-registration files, runner Python scripts, JSON summary verdicts, 8 figures, a comprehensive POC summary document (78 detailed addenda), three Phase XXII synthesis documents, and both v1 and v2 of the manuscript (v2 is current; v1 retained for transparency). All studies reproduce bit-identically across macOS, Linux, and Python 3.9-3.11. KIMORE data (Capecci et al. 2019) and CMU Motion Capture Database real-data anchors are referenced; native JIGSAWS surgical-robot data validation remains future work pending Data Use Agreement approval. Released under CC-BY 4.0.