Privacy-Preserving Operations on Spiral-Domain Encoded Time-Series States: Anonymization, Aggregation, and Federated Learning with Composition Rules and Streaming Variants — Supplementary Reproducibility Archive

Supplementary reproducibility archive for Paper 7 of the spiral-domain encoder validation campaign, targeting IEEE Transactions on Information Forensics and Security (manuscript in preparation).Coverage. 28 pre-registered studies spanning Phases XII–XX of the spiral-domain encoder validation campaign (privacy primitives foundation, edge-case stress, deployment realism, hardware context, composition + streaming, reviewer preemption, Tier-3 strengthening, plus surgical-RT latency). 84 hypotheses, 65 SUPPORTED (77%), 8 honest bounded negatives substantively interpreted.Substantive findings. Three architectural privacy primitives uniquely enabled by spiral encoder's mathematical structure: Primitive A (per-subject angular phase-shift anonymization, k=10 cross-subject anonymity); Primitive B (cross-subject mean aggregation, 8.84 million-fold inversion resistance); Primitive C (federated AR(1) learning, exact 1-round convergence invariant to site count and heterogeneity). 5 deployment embodiments: multi-hospital clinical, multi-factory industrial, federated prosthesis fleet, surgical robotics RT privacy, cloud-scale parallel.Contents.• README.md — submission-package map and reproduction instructions• preregistrations/ — frozen pre-registration .md documents with literal-threshold decision rules• reports/ — per-study .md verdict reports against frozen rules + phase summaries• runners/ — deterministic Python runners (PYTHONHASHSEED=0) reproducing every reported measurement• raw_data/ — per-study CSV outputs and machine-readable JSON verdict blocks• figures/ — manuscript figures (PNG, 300 DPI) + figure-build Python script• code/ — encoder source code (spiral.py), supporting modules (baselines.py, data.py), and package __init__ filesReproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+ and PyTorch 2.8+ (required for the learned-adversary autoencoder attack of Study 86). Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands.Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded.Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 4, 5, 6 archives in this same Zenodo collection.